Instructions to use tsavage68/Interview_L3_1000rate_1e5_SFT_SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsavage68/Interview_L3_1000rate_1e5_SFT_SFT with Transformers:

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

pipe = pipeline("text-generation", model="tsavage68/Interview_L3_1000rate_1e5_SFT_SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tsavage68/Interview_L3_1000rate_1e5_SFT_SFT")
model = AutoModelForCausalLM.from_pretrained("tsavage68/Interview_L3_1000rate_1e5_SFT_SFT", device_map="auto")
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 Settings

vLLM

How to use tsavage68/Interview_L3_1000rate_1e5_SFT_SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsavage68/Interview_L3_1000rate_1e5_SFT_SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsavage68/Interview_L3_1000rate_1e5_SFT_SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tsavage68/Interview_L3_1000rate_1e5_SFT_SFT

SGLang

How to use tsavage68/Interview_L3_1000rate_1e5_SFT_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 "tsavage68/Interview_L3_1000rate_1e5_SFT_SFT" \
    --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": "tsavage68/Interview_L3_1000rate_1e5_SFT_SFT",
		"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 "tsavage68/Interview_L3_1000rate_1e5_SFT_SFT" \
        --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": "tsavage68/Interview_L3_1000rate_1e5_SFT_SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tsavage68/Interview_L3_1000rate_1e5_SFT_SFT with Docker Model Runner:
```
docker model run hf.co/tsavage68/Interview_L3_1000rate_1e5_SFT_SFT
```

Interview_L3_1000rate_1e5_SFT_SFT

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0253

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss
1.3904	0.0376	50	1.2452
1.1582	0.0752	100	0.9397
0.9079	0.1129	150	0.6367
0.3786	0.1505	200	0.4351
0.258	0.1881	250	0.3067
0.2163	0.2257	300	0.2114
0.1031	0.2634	350	0.1570
0.0911	0.3010	400	0.1205
0.0739	0.3386	450	0.0901
0.0503	0.3762	500	0.0713
0.0713	0.4138	550	0.0598
0.066	0.4515	600	0.0457
0.0181	0.4891	650	0.0403
0.015	0.5267	700	0.0358
0.0172	0.5643	750	0.0301
0.0314	0.6020	800	0.0267
0.0279	0.6396	850	0.0259
0.0133	0.6772	900	0.0254
0.0122	0.7148	950	0.0253
0.0126	0.7524	1000	0.0253

Framework versions

Transformers 4.40.2
Pytorch 2.0.0+cu117
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 4

Safetensors

Model size

8B params

Tensor type

F16

Model tree for tsavage68/Interview_L3_1000rate_1e5_SFT_SFT

Base model

meta-llama/Meta-Llama-3-8B-Instruct

Finetuned

(1076)

this model