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

Libraries

How to use izzcw/large_crafting_sft_fail with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("izzcw/large_crafting_sft_fail")
model = AutoModelForCausalLM.from_pretrained("izzcw/large_crafting_sft_fail")
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 izzcw/large_crafting_sft_fail with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/izzcw/large_crafting_sft_fail

SGLang

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

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

izzcw commited on May 8, 2025

Commit

46d886b

verified ·

1 Parent(s): 6674123

Model save

Browse files

Files changed (2) hide show

README.md +94 -0
generation_config.json +12 -0

README.md ADDED Viewed

	@@ -0,0 +1,94 @@

+---
+library_name: transformers
+license: llama3
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+tags:
+- llama-factory
+- generated_from_trainer
+model-index:
+- name: large_crafting_sft_fail
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# large_crafting_sft_fail
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3223
+## 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: 1
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 128
+- total_eval_batch_size: 16
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 1.0
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.5429        | 0.0323 | 50   | 0.4980          |
+| 0.5398        | 0.0646 | 100  | 0.4740          |
+| 0.5484        | 0.0969 | 150  | 0.4833          |
+| 0.5265        | 0.1291 | 200  | 0.4780          |
+| 0.5278        | 0.1614 | 250  | 0.4793          |
+| 0.5259        | 0.1937 | 300  | 0.4519          |
+| 0.5293        | 0.2260 | 350  | 0.4497          |
+| 0.5098        | 0.2583 | 400  | 0.4303          |
+| 0.482         | 0.2906 | 450  | 0.4249          |
+| 0.4683        | 0.3229 | 500  | 0.4224          |
+| 0.4572        | 0.3552 | 550  | 0.4136          |
+| 0.456         | 0.3874 | 600  | 0.4034          |
+| 0.4606        | 0.4197 | 650  | 0.3983          |
+| 0.4285        | 0.4520 | 700  | 0.3874          |
+| 0.4499        | 0.4843 | 750  | 0.3806          |
+| 0.4198        | 0.5166 | 800  | 0.3685          |
+| 0.4208        | 0.5489 | 850  | 0.3661          |
+| 0.4379        | 0.5812 | 900  | 0.3637          |
+| 0.4075        | 0.6134 | 950  | 0.3558          |
+| 0.4121        | 0.6457 | 1000 | 0.3513          |
+| 0.4112        | 0.6780 | 1050 | 0.3454          |
+| 0.4041        | 0.7103 | 1100 | 0.3457          |
+| 0.3852        | 0.7426 | 1150 | 0.3384          |
+| 0.3656        | 0.7749 | 1200 | 0.3340          |
+| 0.384         | 0.8072 | 1250 | 0.3303          |
+| 0.3605        | 0.8395 | 1300 | 0.3276          |
+| 0.3593        | 0.8717 | 1350 | 0.3247          |
+| 0.3624        | 0.9040 | 1400 | 0.3233          |
+| 0.3734        | 0.9363 | 1450 | 0.3229          |
+| 0.3609        | 0.9686 | 1500 | 0.3223          |
+### Framework versions
+- Transformers 4.49.0
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

generation_config.json ADDED Viewed

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

+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128009
+  ],
+  "max_length": 4096,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.49.0"
+}