Instructions to use lmstudio-community/AlchemistCoder-DS-6.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use lmstudio-community/AlchemistCoder-DS-6.7B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lmstudio-community/AlchemistCoder-DS-6.7B-GGUF", filename="AlchemistCoder-DS-6.7B-IQ4_XS.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use lmstudio-community/AlchemistCoder-DS-6.7B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use lmstudio-community/AlchemistCoder-DS-6.7B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lmstudio-community/AlchemistCoder-DS-6.7B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lmstudio-community/AlchemistCoder-DS-6.7B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M
- Ollama
How to use lmstudio-community/AlchemistCoder-DS-6.7B-GGUF with Ollama:
ollama run hf.co/lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M
- Unsloth Studio new
How to use lmstudio-community/AlchemistCoder-DS-6.7B-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for lmstudio-community/AlchemistCoder-DS-6.7B-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for lmstudio-community/AlchemistCoder-DS-6.7B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for lmstudio-community/AlchemistCoder-DS-6.7B-GGUF to start chatting
- Docker Model Runner
How to use lmstudio-community/AlchemistCoder-DS-6.7B-GGUF with Docker Model Runner:
docker model run hf.co/lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M
- Lemonade
How to use lmstudio-community/AlchemistCoder-DS-6.7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull lmstudio-community/AlchemistCoder-DS-6.7B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.AlchemistCoder-DS-6.7B-GGUF-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
)💫 Community Model> AlchemistCoder DS 6.7B by InternLM
👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord.
Model creator: InternLM
Original model: AlchemistCoder-DS-6.7B
GGUF quantization: provided by bartowski based on llama.cpp release b3024
Model Summary:
AlchemistCoder is a series of coding models by InternLM.
This model is tuned from the DeepSeek coder model, and should excel at all coding related tasks.
Prompt template:
Choose the Alpaca preset in your LM Studio.
Under the hood, the model will see a prompt that's formatted like so:
### Instruction:
{prompt}
### Response:
Technical Details
Training details:
- AlchemistPrompts: Designed as data-specific prompts for harmonizing inherent conflicts in multi-source data and mitigating the instruction/response misalignment at a fined-grained level.
- Code Comprehenstion Tasks: Sourced from the process of data construction, consisting of instruction evolution, data filtering, and code review.
- Harmonized Multi-source Data: Instruction tuned on 200M tokens, including 6 types of high-quality data.
- Superior Model Performance: Surpassing all the open-source models of the same size (6.7/7B), and rivaling or even beating larger models (15B/33B/70B/ChatGPT) on 6 code benchmarks.
- Advanced generic capabilities: Demonstrated by the significant improvements on MMLU, BBH, and GSM8K.
For more information, check out their paper here: https://arxiv.org/abs/2405.19265
Special thanks
🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp
🙏 Special thanks to Kalomaze and Dampf for their work on the dataset (linked here) that was used for calculating the imatrix for all sizes.
Disclaimers
LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
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Model tree for lmstudio-community/AlchemistCoder-DS-6.7B-GGUF
Base model
internlm/AlchemistCoder-DS-6.7B
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="lmstudio-community/AlchemistCoder-DS-6.7B-GGUF", filename="", )