Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use Muhammad2003/TriMistral-7B-SLERP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Muhammad2003/TriMistral-7B-SLERP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Muhammad2003/TriMistral-7B-SLERP") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Muhammad2003/TriMistral-7B-SLERP") model = AutoModelForCausalLM.from_pretrained("Muhammad2003/TriMistral-7B-SLERP") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Muhammad2003/TriMistral-7B-SLERP with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Muhammad2003/TriMistral-7B-SLERP" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Muhammad2003/TriMistral-7B-SLERP", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Muhammad2003/TriMistral-7B-SLERP
- SGLang
How to use Muhammad2003/TriMistral-7B-SLERP with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Muhammad2003/TriMistral-7B-SLERP" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Muhammad2003/TriMistral-7B-SLERP", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Muhammad2003/TriMistral-7B-SLERP" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Muhammad2003/TriMistral-7B-SLERP", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Muhammad2003/TriMistral-7B-SLERP with Docker Model Runner:
docker model run hf.co/Muhammad2003/TriMistral-7B-SLERP
| license: apache-2.0 | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| model-index: | |
| - name: TriMistral-7B-SLERP | |
| results: | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: AI2 Reasoning Challenge (25-Shot) | |
| type: ai2_arc | |
| config: ARC-Challenge | |
| split: test | |
| args: | |
| num_few_shot: 25 | |
| metrics: | |
| - type: acc_norm | |
| value: 64.25 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Muhammad2003/TriMistral-7B-SLERP | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: HellaSwag (10-Shot) | |
| type: hellaswag | |
| split: validation | |
| args: | |
| num_few_shot: 10 | |
| metrics: | |
| - type: acc_norm | |
| value: 85.47 | |
| name: normalized accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Muhammad2003/TriMistral-7B-SLERP | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: MMLU (5-Shot) | |
| type: cais/mmlu | |
| config: all | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 64.89 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Muhammad2003/TriMistral-7B-SLERP | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: TruthfulQA (0-shot) | |
| type: truthful_qa | |
| config: multiple_choice | |
| split: validation | |
| args: | |
| num_few_shot: 0 | |
| metrics: | |
| - type: mc2 | |
| value: 53.57 | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Muhammad2003/TriMistral-7B-SLERP | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: Winogrande (5-shot) | |
| type: winogrande | |
| config: winogrande_xl | |
| split: validation | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 79.16 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Muhammad2003/TriMistral-7B-SLERP | |
| name: Open LLM Leaderboard | |
| - task: | |
| type: text-generation | |
| name: Text Generation | |
| dataset: | |
| name: GSM8k (5-shot) | |
| type: gsm8k | |
| config: main | |
| split: test | |
| args: | |
| num_few_shot: 5 | |
| metrics: | |
| - type: acc | |
| value: 59.21 | |
| name: accuracy | |
| source: | |
| url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Muhammad2003/TriMistral-7B-SLERP | |
| name: Open LLM Leaderboard | |
| # merge | |
| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). | |
| ## Merge Details | |
| ### Merge Method | |
| This model was merged using the SLERP merge method. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) | |
| * [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) | |
| * [instructlab/merlinite-7b-lab](https://huggingface.co/instructlab/merlinite-7b-lab) | |
| ### Configuration | |
| Since Slerp allows merging two models at a time, the following YAML configurations were used to produce this model: | |
| ```yaml | |
| slices: | |
| - sources: | |
| - model: HuggingFaceH4/zephyr-7b-beta | |
| layer_range: [0, 32] | |
| - model: NousResearch/Hermes-2-Pro-Mistral-7B | |
| layer_range: [0, 32] | |
| merge_method: slerp | |
| base_model: HuggingFaceH4/zephyr-7b-beta | |
| parameters: | |
| t: | |
| - filter: self_attn | |
| value: [0, 0.5, 0.3, 0.7, 1] | |
| - filter: mlp | |
| value: [1, 0.5, 0.7, 0.3, 0] | |
| - value: 0.5 | |
| dtype: bfloat16 | |
| ``` | |
| Then | |
| ```yaml | |
| slices: | |
| - sources: | |
| - model: ./merge | |
| layer_range: [0, 32] | |
| - model: instructlab/merlinite-7b-lab | |
| layer_range: [0, 32] | |
| merge_method: slerp | |
| base_model: ./merge | |
| parameters: | |
| t: | |
| - filter: self_attn | |
| value: [0, 0.5, 0.3, 0.7, 1] | |
| - filter: mlp | |
| value: [1, 0.5, 0.7, 0.3, 0] | |
| - value: 0.5 | |
| dtype: bfloat16 | |
| ``` | |
| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) | |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Muhammad2003__TriMistral-7B-SLERP) | |
| | Metric |Value| | |
| |---------------------------------|----:| | |
| |Avg. |67.76| | |
| |AI2 Reasoning Challenge (25-Shot)|64.25| | |
| |HellaSwag (10-Shot) |85.47| | |
| |MMLU (5-Shot) |64.89| | |
| |TruthfulQA (0-shot) |53.57| | |
| |Winogrande (5-shot) |79.16| | |
| |GSM8k (5-shot) |59.21| | |