Instructions to use Technoculture/MT7Bi-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Technoculture/MT7Bi-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Technoculture/MT7Bi-sft")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Technoculture/MT7Bi-sft") model = AutoModelForCausalLM.from_pretrained("Technoculture/MT7Bi-sft", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Technoculture/MT7Bi-sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Technoculture/MT7Bi-sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Technoculture/MT7Bi-sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Technoculture/MT7Bi-sft
- SGLang
How to use Technoculture/MT7Bi-sft 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 "Technoculture/MT7Bi-sft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Technoculture/MT7Bi-sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Technoculture/MT7Bi-sft" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Technoculture/MT7Bi-sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Technoculture/MT7Bi-sft with Docker Model Runner:
docker model run hf.co/Technoculture/MT7Bi-sft
File size: 10,242 Bytes
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datasets:
- xzuyn/chatdoctor-200k-stripped
- Technoculture/riddle_sense
- axiong/pmc_llama_instructions
- Open-Orca/SlimOrca-Dedup
language:
- en
tags:
- medical
---

[Technoculture/MT7Bi-alpha](https://huggingface.co/Technoculture/MT7Bi-alpha) adapter merged with its Base Model (Meditron 7B)
# Evaluations
## Open LLM Leaderboard
| Model | ARC |HellaSwag|TruthfulQA|Winogrande|GSM8K|
|---------------------------------------------------|----:|--------:|---------:|---------:|----:|
|[MT7Bi-sft (epoch 4)](https://huggingface.co/Technoculture/MT7Bi-sft)|54.1| 75.11| 43.08| 72.14|15.54|
|[MT7Bi-sft (epoch 1)](https://huggingface.co/Technoculture/MT7Bi)|50.94| 73.24| 43.04| 72.06|22.52|
### Model Evaluation Benchmark
| | | | | | | | | |
| -------- | ------ |----- |----- |----- |----- |----- |----- |------ |
|Category | MT7Bi | meditron-70b | llama-2-70b | med42-70b* | meditron-7b | llama-2-7b | PMC-llama-7b |
|Health | | 81.8 | 69.1 | 83.6 | 27.3 | 16.4 | 3.6 |
|Nutrition | | 77.9 | 68.8 | 62.5 | 31.1 | 12.5 | 6.3 |
|Psychology| | 47.4 | 36.8 | 52.6 | 21.1 | 10.5 | 0.0 |
|Science | | 77.8 | 44.4 | 33.3 | 33.3 | 11.1 | 0.0 |
|Avg | | 71.2 | 54.8 | 58.0 | 28.3 | 12.6 | 2.5 |
| | | | | | | | |
| | | | | | | |
| --- | ------ | ------ |----- |----- |----- |----- |
|Dataset| MT7Bi | meditron-70b | llama-2-70b | med42-70b* | clinical-camel-70b* |
|MMLU-Medical | 46.9 | 77.6 | 77.9 | 74.5 | 65.7 |
|PubMedQA | 65.2 | 81.6 | 80.0 | 61.2 | 67.0 |
|MedMCQA | 42.7 | 66.0 | 62.6 | 59.2 | 46.7 |
|MedQA | | 64.4 | 61.5 | 59.1 | 50.8 |
|MedQA-4-Option| 44.3 | 70.2 | 63.8 | 63.9 | 56.8 |
|Avg | | 72.0 | 69.2 | 63.6 | 57.4 |
| | | | | | | |
| | | | | | | |
| --- | ------ |----- |----- |----- |----- |------ |
|Dataset | meditron-7b | llama-2-7b | pmc-llama-7b | Zephyr-7B-beta* | Mistral-7B-instruct* | MT7Bi |
|MMLU-Medical | 54.2 | 53.7 | 56.4 | 63.3 | 60.0 | 46.9 |
|PubMedQA | 74.4 | 61.8 | 59.2 | 46.0 | 17.8 | 65.2 |
|MedMCQA | 59.2 | 54.4 | 57.6 | 43.0 | 40.2 | 42.7 |
|MedQA | 47.9 | 44.0 | 42.4 | 42.8 | 32.4 | |
|MedQA-4-Option| 52.0 | 49.6 | 49.2 | 48.5 | 41.1 | 44.3 |
|Avg | 57.5 | 52.7 | 53.0 | 48.7 | 38.3 | |
| | | | | | | |
| Model Name | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| ------------------ | -------- | --------- | ---- | ---------- | ---------- | -------- |
| Orca-2-7b | **78.4** | 76.1 | 53.7 | **52.4** | **74.2** | **47.2** |
| LLAMA-2-7b | 43.2 | **77.1** | 44.4 | 38.7 | 69.5 | 16 |
| MT7Bi-sft | 54.1 | 75.11 | - | 43.08 | 72.14 | 15.54 |
### ARC: 54.1%
| Task |Version| Metric | Value | |Stderr|
|-------------|------:|--------------------|-------------|---|------|
|arc_challenge| 1|acc,none | 0.51| | |
| | |acc_stderr,none | 0.01| | |
| | |acc_norm,none | 0.54| | |
| | |acc_norm_stderr,none| 0.01| | |
| | |alias |arc_challenge| | |
### HellaSwag: 75.11%
| Task |Version| Metric | Value | |Stderr|
|---------|------:|--------------------|---------|---|------|
|hellaswag| 1|acc,none | 0.57| | |
| | |acc_stderr,none | 0| | |
| | |acc_norm,none | 0.75| | |
| | |acc_norm_stderr,none| 0| | |
| | |alias |hellaswag| | |
### TruthfulQA: 43.08%
| Task |Version| Metric | Value | |Stderr|
|--------------|-------|-----------------------|-----------------|---|------|
|truthfulqa |N/A |bleu_max,none | 18.31| | |
| | |bleu_max_stderr,none | 0.46| | |
| | |bleu_acc,none | 0.39| | |
| | |bleu_acc_stderr,none | 0| | |
| | |bleu_diff,none | -1.63| | |
| | |bleu_diff_stderr,none | 0.39| | |
| | |rouge1_max,none | 41.99| | |
| | |rouge1_max_stderr,none | 0.71| | |
| | |rouge1_acc,none | 0.39| | |
| | |rouge1_acc_stderr,none | 0| | |
| | |rouge1_diff,none | -2.88| | |
| | |rouge1_diff_stderr,none| 0.66| | |
| | |rouge2_max,none | 27.42| | |
| | |rouge2_max_stderr,none | 0.80| | |
| | |rouge2_acc,none | 0.32| | |
| | |rouge2_acc_stderr,none | 0| | |
| | |rouge2_diff,none | -3.11| | |
| | |rouge2_diff_stderr,none| 0.78| | |
| | |rougeL_max,none | 38.81| | |
| | |rougeL_max_stderr,none | 0.71| | |
| | |rougeL_acc,none | 0.38| | |
| | |rougeL_acc_stderr,none | 0| | |
| | |rougeL_diff,none | -3.01| | |
| | |rougeL_diff_stderr,none| 0.66| | |
| | |acc,none | 0.33| | |
| | |acc_stderr,none | 0.05| | |
| | |alias |truthfulqa | | |
|truthfulqa_gen| 3|bleu_max,none | 18.31| | |
| | |bleu_max_stderr,none | 0.68| | |
| | |bleu_acc,none | 0.39| | |
| | |bleu_acc_stderr,none | 0.02| | |
| | |bleu_diff,none | -1.63| | |
| | |bleu_diff_stderr,none | 0.62| | |
| | |rouge1_max,none | 41.99| | |
| | |rouge1_max_stderr,none | 0.84| | |
| | |rouge1_acc,none | 0.39| | |
| | |rouge1_acc_stderr,none | 0.02| | |
| | |rouge1_diff,none | -2.88| | |
| | |rouge1_diff_stderr,none| 0.81| | |
| | |rouge2_max,none | 27.42| | |
| | |rouge2_max_stderr,none | 0.89| | |
| | |rouge2_acc,none | 0.32| | |
| | |rouge2_acc_stderr,none | 0.02| | |
| | |rouge2_diff,none | -3.11| | |
| | |rouge2_diff_stderr,none| 0.88| | |
| | |rougeL_max,none | 38.81| | |
| | |rougeL_max_stderr,none | 0.84| | |
| | |rougeL_acc,none | 0.38| | |
| | |rougeL_acc_stderr,none | 0.02| | |
| | |rougeL_diff,none | -3.01| | |
| | |rougeL_diff_stderr,none| 0.82| | |
| | |alias | - truthfulqa_gen| | |
|truthfulqa_mc1| 2|acc,none | 0.28| | |
| | |acc_stderr,none | 0.02| | |
| | |alias | - truthfulqa_mc1| | |
|truthfulqa_mc2| 2|acc,none | 0.43| | |
| | |acc_stderr,none | 0.01| | |
| | |alias | - truthfulqa_mc2| | |
### Winogrande: 72.14%
| Task |Version| Metric | Value | |Stderr|
|----------|------:|---------------|----------|---|------|
|winogrande| 1|acc,none | 0.72| | |
| | |acc_stderr,none| 0.01| | |
| | |alias |winogrande| | |
### GSM8K: 15.54%
|Task |Version| Metric |Value| |Stderr|
|-----|------:|-----------------------------|-----|---|------|
|gsm8k| 2|exact_match,get-answer | 0.16| | |
| | |exact_match_stderr,get-answer| 0.01| | |
| | |alias |gsm8k| | |
Elapsed time: 04:06:36 |