gallama 0.1.1

An opinionated Llama Server engine with a focus on agentic tasks

gallama - Guided Agentic Llama

gallama is an opinionated Python library that provides a LLM inference API service backend optimized for local agentic tasks. It focuses on model serving, realtime, multimodal, and local inference integrations rather than multi-agent orchestration.

Gallama is predominantly tested with the Exllama V3 workflow at this point. Other backends are still available, but they may have bugs or rough edges depending on the model and feature path.

Currently, the backend is mainly using Exllama-family backends. Llama.cpp support is under experiment.

Do checkout TabbyAPI if you want a reliable and pure ExllamaV3 API backend.

Key Feature:

• Native Tool Calling
• OpenAI chat completion API
• Anthropic message API
• Compatible with Claude Code

Native Tool Calling

Gallama supports native tool calling. Instead of forcing every model into one synthetic format, Gallama uses the model's own tool-calling format when that format is supported by a parser in src/gallama/backend/llm/prompt_engine/by_model.

Current models with custom native tool parsers:

• Qwen 3
• GLM-4
• Minimax
• Ministral 3

For these models, Gallama expects the model to emit its native tool-call structure, and Gallama parses that structure back into OpenAI-compatible tool_calls or Anthropic-compatible tool_use blocks.

If you want to use a new model with a different native tool-calling format, Gallama will usually need a new parser added under src/gallama/backend/llm/prompt_engine/by_model so the backend can interpret that model correctly. Without a matching parser, tool calling may fail or be decoded incorrectly even if the model itself knows how to call tools.

Reasoning Output

Gallama also returns model reasoning when the model emits it.

With the OpenAI-compatible API, reasoning is returned on the assistant message as reasoning in the raw response payload:

completion = client.chat.completions.create(
    model="qwen3",
    messages=[{"role": "user", "content": "Solve 27 * 43. Give only the answer."}],
)

message = completion.choices[0].message

print(message.content)

# Depending on the SDK version, custom fields may be available either directly
# or through a raw/model-extra view of the response object.
print(getattr(message, "reasoning", None))
print(getattr(message, "model_extra", {}).get("reasoning") if getattr(message, "model_extra", None) else None)

With the Anthropic-compatible API, reasoning is returned as thinking blocks inside response.content:

response = client.messages.create(
    model="qwen3",
    max_tokens=4096,
    thinking={"type": "enabled", "budget_tokens": 1024},
    messages=[{"role": "user", "content": "Solve 27 * 43. Give only the answer."}],
)

thinking_blocks = [block for block in response.content if block.type == "thinking"]
reasoning_text = "\n".join(block.thinking for block in thinking_blocks)

print(reasoning_text)

This makes it possible to inspect the model's intermediate reasoning while still using standard OpenAI or Anthropic client libraries against Gallama.

Quick Start

Head down to the installation guide at the bottom of this page. Then check out the Examples_Notebook.ipynb in the examples folder A simple python streamlit frontend chat UI code is included in the examples folder streamlit Or checkout GallamaUI You can also refer to src/tests folder for more example using OpenAI and Anthropic client.

Features

OpenAI Compatible Server

Fully compatible with the OpenAI client.

Install openai client and overwrite its base setting as follow:

pip install openai

import os
from openai import OpenAI

os.environ['OPENAI_API_KEY'] = 'test'
client = OpenAI(base_url='http://127.0.0.1:8000/v1')

messages = [{"role": "user", "content": "Which is faster in terms of reaction speed: a cat or a dog?"}]

completion = client.chat.completions.create(
    model="mistral",
    messages=messages,
    tool_choice="auto"
)

print(completion)

See src/tests/test_openai.py and src/tests/test_openai_server.py for more complete examples.

Anthropic Compatible Server

Gallama also exposes an Anthropic-compatible Messages endpoint.

Install the Anthropic SDK and point it at your local server:

pip install anthropic

import anthropic

client = anthropic.Anthropic(
    base_url="http://127.0.0.1:8000",
    api_key="test",
)

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Which is faster in terms of reaction speed: a cat or a dog?"}
    ],
)

print(response.content)

See src/tests/test_anthropic.py for a more complete Anthropic client example suite.

Claude Code

You can also point Claude Code at a local Gallama server by overriding the Anthropic base URL and auth token:

ANTHROPIC_BASE_URL="http://127.0.0.1:8000/" ANTHROPIC_AUTH_TOKEN="local" claude --model minimax

This lets Claude Code talk to your local model through Gallama's Anthropic-compatible API.

Function Calling

Supports function calling for all models, mimicking OpenAI's behavior for tool_choice="auto" where if tool usage is not applicable, model will generate normal response.

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        }
    }
]

messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]

completion = client.chat.completions.create(
    model="mistral",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

print(completion.choices[0].message.tool_calls[0].function)

Multiple Concurrent Models

Run multiple models (different or same) with automatic load balancing and request routing. Model VRAM usage can be auto_loaded or with specific GPUs spliting. Each model will be run as a dedicated FastAPI to ensure no threading issue and guarantee speed. However, do note that this will be more demanding on the system as there will be multiple FastAPI running

Basic

gallama run -id "model_id=llama-3.1-8B" -id "model_id=mistral"

Customize GPUs split

gallama run -id "model_id=qwen2-72B gpus=20,15,15,0" -id "model_id=Llama3.1-8B gpus=0,0,0,20"

OpenAI Embedding Endpoint

Utilize Infinity Embedding library for both embedding via OpenAI client.

response = client.embeddings.create(
    input="Your text string for embedding goes here",
    model="Alibaba-NLP/gte-large-en-v1.5"
)

print(response.data[0].embedding)

Legacy OpenAI Completion Endpoint

Support for the Legacy Completion Endpoint.

client.completions.create(
    model="mistral",
    prompt="Tell me a story about a Llama in 200 words",
    max_tokens=1000,
    temperature=0
)

Format Enforcement

Ensure output conforms to specified patterns with a following options that can be specified in the extra_body when using OpenAI client.

completion = client.chat.completions.create(
    model="mistral",
    messages=[{"role": "user", "content": "Is smoking bad for health? Answer with Yes or No"}],
    temperature=0.1,
    max_tokens=200,
    extra_body={
        # "leading_prompt": leading_prompt,         # prefix the generation with some string
        # "regex_pattern": regex_pattern,           # define the regex for the whole generation
        # "regex_prefix_pattern": '(Yes|No)\.',     # define the regex to match the starting words
        # "stop_words": stop_words,                 # define the word to stop generation
    },
)

Streaming

Streaming is fully supported.

messages = [{"role": "user", "content": "Tell me a 200-word story about a Llama"}]

completion = client.chat.completions.create(
    model="mistral",
    messages=messages,
    stream=True,
    temperature=0.1,
)

for chunk in completion:
    print(chunk.choices[0].delta.content, end='')

Remote Model Management

Load and unload models via API calls.

start gallama server if it is not current running:

gallama run

import requests

api_url = "http://127.0.0.1:8000/add_model"

payload = [
    {
        "model_id": "qwen-2-72B",
        "gpus": [22,22,4,0],
        "cache_size": 32768,
    },
    {
        "model_id": "gemma-2-9b",
        "gpus": [0,0,0,12],
        "cache_size": 32768,
    },
    {
        "model_id": "multilingual-e5-large-instruct",
        "gpus": [0,0,0,5],
    },
]

response = requests.post(api_url, json=payload)

Installation

gallama requires certain components to be installed and functioning.

Ensure that you have a working backend installed before using Gallama. In practice, Exllama V3 is the backend I test against most often. Other backends may still work, but they may need extra debugging depending on the model and feature set.

OS level package required as followed: For Speech to Text, You will need to install the dependency as required by faster whisper Most notably is CuDNN https://developer.nvidia.com/cudnn

For Text to Speech, install the following package:

apt-get install portaudio19-dev ffmpeg

Now to install gallama from pip

pip install gallama

Optional extras are available if you only want specific components:

pip install "gallama[all]"
pip install "gallama[exl2]"
pip install "gallama[exl3]"
pip install "gallama[llama-cpp]"
pip install "gallama[transformers-backend]"
pip install "gallama[utils]"
pip install "gallama[embedding]"
pip install "gallama[stt]"
pip install "gallama[tts]"
pip install "gallama[video]"
pip install "gallama[vllm]"
pip install "gallama[sglang]"

Extras can be combined as needed:

pip install "gallama[exl3,tts]"
pip install "gallama[llama-cpp,stt]"

For newer model support, the latest transformers release is often needed. If a model is not loading correctly, update it with:

pip install -U transformers

Or, install from source:

git clone https://github.com/remichu-ai/gallama.git
cd gallama
pip install .

If you're starting from scratch and don't have these dependencies yet, follow these steps:

1. Create a virtual environment (recommended): Recommend to use python 3.12 if you can or minimally 3.11 for future tensor parallel compatibility.

   conda create --name genv python=3.12
   conda activate genv
   

2. Install and verify your backend:

   • Exllama V3 is the recommended path if you want the setup closest to what is actively tested.
   • Exllama V2, llama.cpp, transformers, vLLM, sglang, and other backends are still available, but expect some backend-specific rough edges.

   (Optional) Install llama cpp-python:

   • Follow instructions at llama-cpp-python
   • Test with examples from llama-cpp-python Examples

3. (Optional) Install Flash Attention for improved performance:

   • Follow instructions at Flash Attention GitHub

4. (Optional) Install Llama.cpp:

   • Follow instructions at Llama-cpp-python
   • Note: ExLlamaV2 is currently recommended. Llama.cpp support is under development.

5. Install gallama:

   pip install gallama
   

   Or install with only the components you need:

   pip install "gallama[all]"
   pip install "gallama[exl3]"
   pip install "gallama[exl2,stt]"
   

   Or, install from source:

   git clone https://github.com/remichu-ai/gallama.git
   cd gallama
   pip install ".[all]"
   

Usage

Follow these steps to use the model.

Setup

1. Initialize gallama:

   gallama run
   

   This creates a model_config.yaml file in ~/gallama.

2. Update ~/gallama/model_config.yaml with your model configurations.

3. Launch the model: Simple method

   gallama run mistral
   

   Advanced method

   gallama run -id "model_id=mistral"
   

model_config.yaml

Each top-level key is the model name that Gallama will expose through the API. The value under that key is the configuration used to load the backend.

Minimal Exllama example:

mistral:
  backend: exllama
  model_id: /home/your-user/gallama/models/Mistral-7B-instruct-v0.3-4.5bpw-exl2
  prompt_template: Mistral_large
  gpus: auto

Typical keys:

• backend: backend name such as exllama, llama_cpp, transformers, embedding, kokoro, or gpt_sovits
• model_id: local path to the model or model directory
• prompt_template: prompt formatter to use for the model family
• gpus: usually auto, but can also be a per-GPU split
• max_seq_len: override context length if needed
• cache_quant: KV cache quantization such as FP16, Q4, Q6, or Q8
• quant: optional metadata for the model quantization you downloaded
• eos_token_list: optional extra EOS tokens for models that need them
• backend_extra_args: backend-specific options, commonly used for transformers, sglang, gpt_sovits, and similar backends

Example with a transformers backend:

llama-3.2-Vision-11B_transformers:
  backend: transformers
  model_id: /home/your-user/gallama/models/llama-3.2-Vision-11B-4.0bpw-transformers
  prompt_template: Llama3.2-VL
  gpus: auto
  cache_quant: Q4
  quant: 4.0
  backend_extra_args:
    model_class: transformers.MllamaForConditionalGeneration
    tokenizer_class: transformers.AutoTokenizer
    processor_class: transformers.AutoProcessor
    model_class_extra_kwargs:
      attn_implementation: sdpa

Example with a llama_cpp backend:

codestral_llama_cpp:
  backend: llama_cpp
  model_id: /home/your-user/gallama/models/codestral-4.0bpw-llama_cpp/Codestral-22B-v0.1-Q4_K_M.gguf
  prompt_template: Mistral
  gpus: auto
  cache_quant: Q4
  quant: 4.0

Example with TTS voice presets:

gpt_sovits:
  backend: gpt_sovits
  model_id: /home/your-user/gallama/models/gpt_sovits
  backend_extra_args:
    device: cuda
    is_half: false
    version: v2
    chunk_size_in_s: 0.1
    bert_base_path: /home/your-user/gallama/models/gpt_sovits/pretrained_models/chinese-roberta-wwm-ext-large
    cnhuhbert_base_path: /home/your-user/gallama/models/gpt_sovits/pretrained_models/chinese-hubert-base
    t2s_weights_path: /home/your-user/gallama/models/gpt_sovits/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
    vits_weights_path: /home/your-user/gallama/models/gpt_sovits/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
  voice:
    shenhe:
      language: en
      ref_audio_path: /path/to/reference.wav
      ref_audio_transcription: I'm not trying to save the world...
      speed_factor: 1.1

Notes:

• Use the YAML key itself as the API model name. For example, if the key is qwen-2.5-32B, then that is the model string to pass in the client request.
• prompt_template matters. If the wrong one is chosen, the model may still load but chat quality or tool use can break.
• backend_extra_args is the place for backend-specific tuning such as custom tokenizer/model/processor classes or TTS model paths.
• You can keep your Gallama config in another location by setting GALLAMA_HOME_PATH.

Advanced Usage

Using gallama run -id followed by a string which is a dictionary of key-value pair will unlock additional option as following:

Customize the model launch using various parameters. Available parameters for the -id option include:

• model_name: API model name to expose from Gallama. Required when running without a matching model_config.yaml entry.
• model_id: Model path or Hugging Face repo ID. Required for YAML-free launch and optional when it already exists in model_config.yaml.
• gpus: VRAM usage for each GPU, comma-separated list of floats (optional)
• cache_size: Context length for cache text in integers (optional)
• cache_quant: Quantization to use for cache, options are "FP16", "Q4", "Q6", "Q8" (optional)
• max_seq_len: Maximum sequence length (optional)
• backend: Model engine backend. Options include exllama, exllamav3, llama_cpp, transformers, vllm, sglang, mlx_vlm, embedding, faster_whisper, mlx_whisper, gpt_sovits, kokoro.
• tp: enable tensor parallel with exllama v2 (experimental). See further below

Run Without model_config.yaml

If you fully specify the model on the CLI, Gallama can run it without a matching entry in ~/gallama/model_config.yaml.

Minimum required arguments for a YAML-free LLM launch:

• model_name
• model_id
• backend

Example:

gallama run -id "model_name=minimax model_id=/path/to/model backend=exllamav3"

To also write the same CLI logs to a file:

gallama run -id "model_name=minimax model_id=/path/to/model backend=exllamav3" `--log-file ./log/gallama.log`

Useful optional arguments:

• max_seq_len=32768
• gpus=20,20 or leave it as automatic
• cache_size=32768
• cache_quant=Q4
• prompt_template=<template-name>
• strict=True
• max_concurrent_requests=<n>
• --log-file ./log/gallama.log to mirror CLI logs into a file

Notes:

• If you omit prompt_template, Gallama will use the tokenizer's built-in Hugging Face chat template. That is usually fine for modern transformers models, but older or custom models may still need an explicit prompt template.
• Draft/speculative decoding still expects the draft model to exist in model_config.yaml unless you pass a full draft_model_id directly.

Speculative Decoding Parameters

• draft_model_id: ID of the draft model (optional)
• draft_model_name: Name of the draft model (optional)
• draft_gpus: VRAM usage for each GPU for the draft model, comma-separated list of floats (optional)
• draft_cache_size: Context length for cache text in integers for the draft model (optional)
• draft_cache_quant: Quantization to use for cache for the draft model, options are "FP16", "Q4", "Q6", "Q8" (optional)

Examples

1. Launch two models simultaneously:

   gallama run -id "model_name=mistral model_id=/path/to/mistral backend=exllamav3" -id "model_name=llama3 model_id=/path/to/llama3 backend=exllamav3"
   

2. Launch a model with specific VRAM limits per GPU:

   gallama run -id "model_name=qwen2-72B model_id=/path/to/qwen2-72B backend=exllamav3 gpus=22,22,10,0"
   

   This limits memory usage to 22GB for GPU0 and GPU1, 10GB for GPU2, and 0GB for GPU3.

3. Launch a model with custom cache size and quantization: By default cache_size is initialized to max sequence length of the model. However, if there is VRAM to spare, increase cache_size will have model to perform better for concurrent and batched request. By default, cache_quant=Q4 will be used. However, do adjust it if required e.g. Qwen2 1.5B doesn't work well with Q4 cache, please use Q6 or Q8.

   gallama run -id "model_name=mistral model_id=/path/to/mistral backend=exllamav3 cache_size=102400 cache_quant=Q8"
   

4. Launch a model with reduced cache size and quantization: For model with high context, lower the sequence length can significantly reduce VRAM usage. e.g. Mistral Large 2 can handle 128K content, however, it will require significant VRAM for the cache

   gallama run -id "model_name=mistral_large model_id=/path/to/mistral_large backend=exllamav3 max_seq_len=32768"
   

5. Launch a model for embedding:

   gallama run -id "model_name=gte-large-en-v1.5 model_id=Alibaba-NLP/gte-large-en-v1.5 backend=embedding"
   

6. Launch a model with speculative decoding: Only model with same vocabulary should be used for speculative decoding. For reference, by enabling speculative decoding, qwen2-72B generation speed improve from 20tok/s to 25-35tok/s on my 4090s. Highly recommend speculative decoding if you have VRAM to spare.

   gallama run -id "model_name=qwen2-72B model_id=/path/to/qwen2-72B backend=exllama draft_model_id=/path/to/qwen2-1.5B"
   

   Ensure your GPU settings can accommodate the model requirements. Trial and adjust parameters as needed for your specific use case. Note: The backend is assumed to be the same for both the main model and the draft model in speculative decoding.

7. Tensor Parallel (TP) Exllama V2 Tensor Parallel support Tensor Parallel from v0.1.9.

   • Update your python>=3.11
   • Install ExllamaV2>=0.1.9
   • Only support Qwen2-72B, Llama3.1-70B and Mistral Large at the moment
   • Do run a draft model to help further with speed (Qwen2-1.5B, Llama3.1-8B, Mistral v0.3 respectively) To enable tensor parallel, simply add tp=True Exllama tensor parallel support parallelism on odd number of GPUs. Also exact matching of GPU is not requirement The speed boost for TP for dense model is huge (close to X1.5-X2).

   gallama run -id "model_name=qwen-2-72B model_id=/path/to/qwen-2-72B backend=exllama draft_model_id=/path/to/qwen-2-1.5B tp=True"
   

8. Others If you keep gallama config folder in another location instead of ~home/gallama then you can set env parameter GALLAMA_HOME_PATH when running.

OpenAI realtime websocket (Experimental)

From version 0.0.9, gallama does provide a OpenAI Realtime websocket by wrapping Websocker over a TTS + LLM + TTS setup. While this is not true Sound to Sound set up, it does provide a mock-up of OpenAI realtime websocket for testing. The setup also provide integration with Video from Livekit for video voice chat app.

The Realtime Websocket API is tested working with follow:

• https://github.com/livekit-examples/realtime-playground.git
• https://github.com/openai/openai-realtime-console/tree/websockets

API Spec:

• https://platform.openai.com/docs/guides/realtime

To Use Video Chat feature Please refer to the PAI app here:

• https://github.com/remichu-ai/pai.git
• https://github.com/remichu-ai/pai-agent.git

Do note that there are some package at Linux level that you will need to install. Refer to installation portion below. While it does mimic openai realtime, there could be bug due to it not using native audio to audio model

Legacy Model Downloader

The built-in model downloader is now considered outdated.

The preferred workflow is:

1. Download or prepare your model manually using your normal tooling.
2. Put it wherever you want on disk.
3. Add or update the corresponding entry in ~/gallama/model_config.yaml.

The legacy downloader commands may still exist in parts of the codebase, but they are no longer the recommended way to manage models.