Instructions to use mlfoundations-dev/stackexchange_engineering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlfoundations-dev/stackexchange_engineering with Transformers:

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

pipe = pipeline("text-generation", model="mlfoundations-dev/stackexchange_engineering")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlfoundations-dev/stackexchange_engineering")
model = AutoModelForCausalLM.from_pretrained("mlfoundations-dev/stackexchange_engineering")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mlfoundations-dev/stackexchange_engineering with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlfoundations-dev/stackexchange_engineering"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlfoundations-dev/stackexchange_engineering",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlfoundations-dev/stackexchange_engineering

SGLang

How to use mlfoundations-dev/stackexchange_engineering 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 "mlfoundations-dev/stackexchange_engineering" \
    --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": "mlfoundations-dev/stackexchange_engineering",
		"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 "mlfoundations-dev/stackexchange_engineering" \
        --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": "mlfoundations-dev/stackexchange_engineering",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mlfoundations-dev/stackexchange_engineering with Docker Model Runner:
```
docker model run hf.co/mlfoundations-dev/stackexchange_engineering
```

sedrickkeh commited on Dec 26, 2024

Commit

77189de

verified ·

1 Parent(s): a858792

End of training

Browse files

Files changed (7) hide show

README.md +2 -1
all_results.json +12 -0
eval_results.json +7 -0
train_results.json +8 -0
trainer_state.json +115 -0
training_eval_loss.png +0 -0
training_loss.png +0 -0

README.md CHANGED Viewed

@@ -4,6 +4,7 @@ license: llama3.1
 base_model: meta-llama/Meta-Llama-3.1-8B
 tags:
 - llama-factory
 - generated_from_trainer
 model-index:
 - name: stackexchange_engineering
@@ -15,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
 # stackexchange_engineering
-This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on an unknown dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.9366

 base_model: meta-llama/Meta-Llama-3.1-8B
 tags:
 - llama-factory
+- full
 - generated_from_trainer
 model-index:
 - name: stackexchange_engineering
 # stackexchange_engineering
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the mlfoundations-dev/stackexchange_engineering dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.9366

all_results.json ADDED Viewed

	@@ -0,0 +1,12 @@

+{
+    "epoch": 2.926829268292683,
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+    "eval_steps_per_second": 0.624,
+    "total_flos": 125418413752320.0,
+    "train_loss": 0.9610127830505371,
+    "train_runtime": 2801.3394,
+    "train_samples_per_second": 14.047,
+    "train_steps_per_second": 0.027
+}

eval_results.json ADDED Viewed

	@@ -0,0 +1,7 @@

+{
+    "epoch": 2.926829268292683,
+    "eval_loss": 0.9366496801376343,
+    "eval_runtime": 17.6253,
+    "eval_samples_per_second": 39.205,
+    "eval_steps_per_second": 0.624
+}

train_results.json ADDED Viewed

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

+{
+    "epoch": 2.926829268292683,
+    "total_flos": 125418413752320.0,
+    "train_loss": 0.9610127830505371,
+    "train_runtime": 2801.3394,
+    "train_samples_per_second": 14.047,
+    "train_steps_per_second": 0.027
+}

trainer_state.json ADDED Viewed

	@@ -0,0 +1,115 @@

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+    }
+  ],
+  "logging_steps": 10,
+  "max_steps": 75,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 3,
+  "save_steps": 500,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
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+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": true
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+  "train_batch_size": 8,
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+}

training_eval_loss.png ADDED Viewed

training_loss.png ADDED Viewed