Instructions to use anudaw/temp-0.5-distilled-code-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anudaw/temp-0.5-distilled-code-llama with Transformers:

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

pipe = pipeline("text-generation", model="anudaw/temp-0.5-distilled-code-llama")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anudaw/temp-0.5-distilled-code-llama")
model = AutoModelForCausalLM.from_pretrained("anudaw/temp-0.5-distilled-code-llama")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use anudaw/temp-0.5-distilled-code-llama with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/anudaw/temp-0.5-distilled-code-llama

SGLang

How to use anudaw/temp-0.5-distilled-code-llama 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 "anudaw/temp-0.5-distilled-code-llama" \
    --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": "anudaw/temp-0.5-distilled-code-llama",
		"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 "anudaw/temp-0.5-distilled-code-llama" \
        --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": "anudaw/temp-0.5-distilled-code-llama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use anudaw/temp-0.5-distilled-code-llama with Docker Model Runner:
```
docker model run hf.co/anudaw/temp-0.5-distilled-code-llama
```

anudaw commited on May 1, 2024

Commit

2eebf12

verified ·

1 Parent(s): 018fc96

Model save

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Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -1,6 +1,6 @@
 ---
 license: apache-2.0
-base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 tags:
 - trl
 - sft
@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
 # temp-0.5-distilled-code-llama
-This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset.
 ## Model description
@@ -43,7 +43,7 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: constant
 - lr_scheduler_warmup_ratio: 0.03
-- num_epochs: 2
 ### Framework versions

 ---
 license: apache-2.0
+base_model: anudaw/temp-0.5-distilled-code-llama
 tags:
 - trl
 - sft
 # temp-0.5-distilled-code-llama
+This model is a fine-tuned version of [anudaw/temp-0.5-distilled-code-llama](https://huggingface.co/anudaw/temp-0.5-distilled-code-llama) on an unknown dataset.
 ## Model description
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: constant
 - lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 5
 ### Framework versions