Instructions to use ed001/datascience-coder-1.3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ed001/datascience-coder-1.3b with Transformers:

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

pipe = pipeline("text-generation", model="ed001/datascience-coder-1.3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ed001/datascience-coder-1.3b")
model = AutoModelForCausalLM.from_pretrained("ed001/datascience-coder-1.3b")
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

vLLM

How to use ed001/datascience-coder-1.3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ed001/datascience-coder-1.3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ed001/datascience-coder-1.3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ed001/datascience-coder-1.3b

SGLang

How to use ed001/datascience-coder-1.3b 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 "ed001/datascience-coder-1.3b" \
    --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": "ed001/datascience-coder-1.3b",
		"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 "ed001/datascience-coder-1.3b" \
        --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": "ed001/datascience-coder-1.3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ed001/datascience-coder-1.3b with Docker Model Runner:
```
docker model run hf.co/ed001/datascience-coder-1.3b
```

ed001 commited on Jan 1, 2024

Commit

52a3252

1 Parent(s): 4e1f447

add training details

Browse files

Files changed (1) hide show

README.md +17 -0

README.md CHANGED Viewed

@@ -35,6 +35,23 @@ result = pipe(build_instruction_prompt("Perform EDA on the Iris dataset"))
 print(result[0]['generated_text'])
 ```
 ## Examples

 print(result[0]['generated_text'])
 ```
+## Training Details
+lora_r: 16
+lora_alpha: 8
+lora_dropout: 0.05
+target_modules: q, k, v, o, gate_proj, down_proj, up_proj, lm_head
+weight_decay: 0
+optmizer: paged_adamw_32bit
+lr: 1e-4
+lr_scheduler: cosine
+max_seq_len: 4096
+batch_size: 4
+max_grad_norm: 0.5
+warmup_ratio: 0.05
+num_epochs: 1
+Training was performed on the python subset of the ds-coder-instruct dataset.
 ## Examples