Instructions to use Minbyul/meditron-7b-wo-kqa_silver_wogold-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Minbyul/meditron-7b-wo-kqa_silver_wogold-sft with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Minbyul/meditron-7b-wo-kqa_silver_wogold-sft")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Minbyul/meditron-7b-wo-kqa_silver_wogold-sft")
model = AutoModelForMultimodalLM.from_pretrained("Minbyul/meditron-7b-wo-kqa_silver_wogold-sft")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Minbyul/meditron-7b-wo-kqa_silver_wogold-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Minbyul/meditron-7b-wo-kqa_silver_wogold-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Minbyul/meditron-7b-wo-kqa_silver_wogold-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Minbyul/meditron-7b-wo-kqa_silver_wogold-sft

SGLang

How to use Minbyul/meditron-7b-wo-kqa_silver_wogold-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 "Minbyul/meditron-7b-wo-kqa_silver_wogold-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": "Minbyul/meditron-7b-wo-kqa_silver_wogold-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 "Minbyul/meditron-7b-wo-kqa_silver_wogold-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": "Minbyul/meditron-7b-wo-kqa_silver_wogold-sft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Minbyul/meditron-7b-wo-kqa_silver_wogold-sft with Docker Model Runner:
```
docker model run hf.co/Minbyul/meditron-7b-wo-kqa_silver_wogold-sft
```

Minbyul commited on Apr 16, 2024

Commit

e2b5caa

verified ·

1 Parent(s): 021b15d

End of training

Browse files

Files changed (4) hide show

README.md +6 -2
all_results.json +5 -0
config.json +1 -1
eval_results.json +8 -0

README.md CHANGED Viewed

@@ -2,11 +2,15 @@
 license: llama2
 base_model: epfl-llm/meditron-7b
 tags:
 - trl
 - sft
 - generated_from_trainer
 datasets:
-- generator
 model-index:
 - name: meditron-7b-wo-kqa_silver_wogold-sft
   results: []
@@ -17,7 +21,7 @@ should probably proofread and complete it, then remove this comment. -->
 # meditron-7b-wo-kqa_silver_wogold-sft
-This model is a fine-tuned version of [epfl-llm/meditron-7b](https://huggingface.co/epfl-llm/meditron-7b) on the generator dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.8975

 license: llama2
 base_model: epfl-llm/meditron-7b
 tags:
+- alignment-handbook
+- trl
+- sft
+- generated_from_trainer
 - trl
 - sft
 - generated_from_trainer
 datasets:
+- HuggingFaceH4/deita-10k-v0-sft
 model-index:
 - name: meditron-7b-wo-kqa_silver_wogold-sft
   results: []
 # meditron-7b-wo-kqa_silver_wogold-sft
+This model is a fine-tuned version of [epfl-llm/meditron-7b](https://huggingface.co/epfl-llm/meditron-7b) on the HuggingFaceH4/deita-10k-v0-sft dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.8975

all_results.json CHANGED Viewed

@@ -1,5 +1,10 @@
 {
     "epoch": 2.61,
     "train_loss": 1.0021109501520793,
     "train_runtime": 367.7198,
     "train_samples": 4044,

 {
     "epoch": 2.61,
+    "eval_loss": 0.8974742889404297,
+    "eval_runtime": 39.1827,
+    "eval_samples": 4948,
+    "eval_samples_per_second": 11.306,
+    "eval_steps_per_second": 0.715,
     "train_loss": 1.0021109501520793,
     "train_runtime": 367.7198,
     "train_samples": 4044,

config.json CHANGED Viewed

@@ -23,6 +23,6 @@
   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.39.0.dev0",
-  "use_cache": false,
   "vocab_size": 32017
 }

   "tie_word_embeddings": false,
   "torch_dtype": "bfloat16",
   "transformers_version": "4.39.0.dev0",
+  "use_cache": true,
   "vocab_size": 32017
 }

eval_results.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
+    "epoch": 2.61,
+    "eval_loss": 0.8974742889404297,
+    "eval_runtime": 39.1827,
+    "eval_samples": 4948,
+    "eval_samples_per_second": 11.306,
+    "eval_steps_per_second": 0.715
+}