Instructions to use pandyamarut/SeedCoder-Final-CP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pandyamarut/SeedCoder-Final-CP with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "pandyamarut/SeedCoder-Final-CP")

Transformers

How to use pandyamarut/SeedCoder-Final-CP with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pandyamarut/SeedCoder-Final-CP")
model = AutoModelForCausalLM.from_pretrained("pandyamarut/SeedCoder-Final-CP")
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 pandyamarut/SeedCoder-Final-CP with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/pandyamarut/SeedCoder-Final-CP

SGLang

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

Docker Model Runner
How to use pandyamarut/SeedCoder-Final-CP with Docker Model Runner:
```
docker model run hf.co/pandyamarut/SeedCoder-Final-CP
```

See axolotl config

axolotl version: 0.13.0.dev0

adapter: lora
base_model: ByteDance-Seed/Seed-Coder-8B-Instruct
bf16: true
dataset_prepared_path: last_run_prepared

# Dataset configuration for instruction/input/output format
datasets:
  - path: dataset.jsonl
    type: alpaca  # Changed from chat_template to alpaca for instruction/input/output format

debug: null
deepspeed: /osmosis/zero2.json
early_stopping_patience: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
group_by_length: false
learning_rate: 0.0001
liger_fused_linear_cross_entropy: true
liger_glu_activation: true
liger_layer_norm: true
liger_rms_norm: true
liger_rope: true
load_in_4bit: false
load_in_8bit: false
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1
micro_batch_size: 16
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_torch
output_dir: ./lora-out-seedcoder
pad_to_sequence_len: true
plugins:
- axolotl.integrations.liger.LigerPlugin
resume_from_checkpoint: null
sample_packing: false
save_steps: 60
save_total_limit: 100
sequence_len: 4096
# special_tokens:
#   eos_token: <|im_end|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: test-aa
wandb_project: seedcoder
wandb_log_model: null
wandb_name: No-mods-bytedance-seedcoder-8b-instruct-lora-64
wandb_watch: null
warmup_ratio: 0.05
weight_decay: 0.0
xformers_attention: null

lora-out-seedcoder

This model is a fine-tuned version of ByteDance-Seed/Seed-Coder-8B-Instruct on the dataset.jsonl dataset.

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: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 64
total_eval_batch_size: 64
optimizer: Use OptimizerNames.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_steps: 21
training_steps: 436

Training results

Framework versions

PEFT 0.17.1
Transformers 4.57.1
Pytorch 2.8.0+cu128
Datasets 4.3.0
Tokenizers 0.22.1

Downloads last month: 4

Model tree for pandyamarut/SeedCoder-Final-CP

Base model

ByteDance-Seed/Seed-Coder-8B-Base

Finetuned

ByteDance-Seed/Seed-Coder-8B-Instruct

Adapter

(7)

this model