Instructions to use TRAC-MTRY/traclm-v2-7b-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TRAC-MTRY/traclm-v2-7b-instruct-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TRAC-MTRY/traclm-v2-7b-instruct-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use TRAC-MTRY/traclm-v2-7b-instruct-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf TRAC-MTRY/traclm-v2-7b-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf TRAC-MTRY/traclm-v2-7b-instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf TRAC-MTRY/traclm-v2-7b-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf TRAC-MTRY/traclm-v2-7b-instruct-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf TRAC-MTRY/traclm-v2-7b-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf TRAC-MTRY/traclm-v2-7b-instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/TRAC-MTRY/traclm-v2-7b-instruct-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use TRAC-MTRY/traclm-v2-7b-instruct-GGUF with Ollama:
ollama run hf.co/TRAC-MTRY/traclm-v2-7b-instruct-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use TRAC-MTRY/traclm-v2-7b-instruct-GGUF with Docker Model Runner:
docker model run hf.co/TRAC-MTRY/traclm-v2-7b-instruct-GGUF:Q4_K_M
- Lemonade
How to use TRAC-MTRY/traclm-v2-7b-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TRAC-MTRY/traclm-v2-7b-instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.traclm-v2-7b-instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Model Card for traclm-v2-7b-instruct-GGUF
This repo contains several GGUF quantizations of TRAC-MTRY/traclm-v2-7b-instruct for utilization of the model on low-resource hardware.
Available quantizations are listed here:
| Name | Quant method | Bits | Size | Use case |
|---|---|---|---|---|
| TRAC-MTRY/traclm-v2-7b-instruct-2q_k | Q2_K | 2 | 2.83 GB | smallest, significant quality loss - not recommended for most purposes |
| TRAC-MTRY/traclm-v2-7b-instruct-3q_k_m | Q3_K_M | 3 | 3.3 GB | very small, high quality loss |
| TRAC-MTRY/traclm-v2-7b-instruct-4q_k_m | Q4_K_M | 4 | 4.08 GB | medium, balanced quality - recommended |
| TRAC-MTRY/traclm-v2-7b-instruct-5q_k_m | Q5_K_M | 5 | 4.78 GB | large, very low quality loss - recommended |
| TRAC-MTRY/traclm-v2-7b-instruct-6q_k_m | Q6_K | 6 | 5.53 GB | very large, extremely low quality loss |
Note: an fp16 unquantized version in GGUF format is also provided, see repo files.
Read more about GGUF quantization here.
Read more about the unquantized model here.
Prompt Format
This model was fine-tuned with the alpaca prompt format. It is highly recommended that you use the same format for any interactions with the model. Failure to do so will degrade performance significantly.
Standard Alpaca Format:
### System:\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n\n\n\n### Instruction:\n{prompt}\n\n### Response:\n "
Input Field Variant:
### System:\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n\n\n\n### Instruction:\n{prompt}\n\n###Input:\n{input}\n\n### Response:\n "
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