Instructions to use caikybaldo999/webcoder-100m-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use caikybaldo999/webcoder-100m-instruct with Transformers:

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

pipe = pipeline("text-generation", model="caikybaldo999/webcoder-100m-instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("caikybaldo999/webcoder-100m-instruct")
model = AutoModelForCausalLM.from_pretrained("caikybaldo999/webcoder-100m-instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use caikybaldo999/webcoder-100m-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "caikybaldo999/webcoder-100m-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caikybaldo999/webcoder-100m-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/caikybaldo999/webcoder-100m-instruct

SGLang

How to use caikybaldo999/webcoder-100m-instruct 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 "caikybaldo999/webcoder-100m-instruct" \
    --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": "caikybaldo999/webcoder-100m-instruct",
		"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 "caikybaldo999/webcoder-100m-instruct" \
        --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": "caikybaldo999/webcoder-100m-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use caikybaldo999/webcoder-100m-instruct with Docker Model Runner:
```
docker model run hf.co/caikybaldo999/webcoder-100m-instruct
```

webcoder-100m-instruct / training_config.json

caikybaldo999

Full SFT WebCoder-100M-Instruct | 78,226,740 SFT tokens

062ffb1 verified about 17 hours ago

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	{
	"base_model": "caikybaldo999/webcoder-100m-html-css-js",
	"output_repo": "caikybaldo999/webcoder-100m-instruct",
	"dataset_id": "iamtarun/code_instructions_120k_alpaca",
	"max_examples": 40000,
	"validation_fraction": 0.03,
	"max_seq_len": 1024,
	"epochs": 10,
	"micro_batch_size": 16,
	"grad_accum_steps": 4,
	"learning_rate": 5e-05,
	"min_lr_ratio": 0.1,
	"warmup_ratio": 0.05,
	"weight_decay": 0.05,
	"grad_clip": 1.0,
	"max_sft_tokens": 150000000,
	"log_every_updates": 10,
	"eval_every_updates": 100,
	"save_every_updates": 250,
	"eval_batches": 50,
	"work_dir": "/content/webcoder_instruct",
	"use_google_drive": false,
	"drive_dir": "/content/drive/MyDrive/WebCoder100M-Instruct",
	"hub_private": false
	}