Instructions to use pandyamarut/nov26-sc-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pandyamarut/nov26-sc-lora 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/nov26-sc-lora")

Transformers

How to use pandyamarut/nov26-sc-lora with Transformers:

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

pipe = pipeline("text-generation", model="pandyamarut/nov26-sc-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pandyamarut/nov26-sc-lora")
model = AutoModelForCausalLM.from_pretrained("pandyamarut/nov26-sc-lora")
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/nov26-sc-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pandyamarut/nov26-sc-lora"
# 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/nov26-sc-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pandyamarut/nov26-sc-lora

SGLang

How to use pandyamarut/nov26-sc-lora 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/nov26-sc-lora" \
    --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/nov26-sc-lora",
		"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/nov26-sc-lora" \
        --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/nov26-sc-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pandyamarut/nov26-sc-lora with Docker Model Runner:
```
docker model run hf.co/pandyamarut/nov26-sc-lora
```

See axolotl config

axolotl version: 0.13.0.dev0

# # adapter: lora
# # base_model: Qwen/Qwen3-8B
# # bf16: true
# # dataset_prepared_path: last_run_prepared

# # # Dataset configuration for instruction/input/output format
# # datasets:
# # - chat_template: tokenizer_default
# #   field_messages: messages
# #   message_field_content: content
# #   message_field_role: role
# #   path: data_clean.jsonl
# #   roles:
# #     assistant:
# #     - assistant
# #     system:
# #     - system
# #     user:
# #     - user
# #   type: chat_template

# # 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: ./outQwen
# # 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: qwen-data-pattern
# # wandb_watch: null
# # warmup_ratio: 0.05
# # weight_decay: 0.0
# # xformers_attention: null
# adapter: lora
# base_model: osmosis-ai/sourcegraph-coder-8b-v1
# bf16: true
# dataset_prepared_path: last_run_prepared

# # Dataset configuration for instruction/input/output format
# datasets:
# - chat_template: tokenizer_default
#   field_messages: messages
#   message_field_content: content
#   message_field_role: role
#   path: data_clean.jsonl
#   roles:
#     assistant:
#     - assistant
#     system:
#     - system
#     user:
#     - user
#   type: chat_template

# debug: null
# deepspeed: /osmosis/zero2.json
# early_stopping_patience: null
# flash_attention: true
# fp16: null
# fsdp: null
# fsdp_config: null
# gradient_accumulation_steps: 4
# gradient_checkpointing: true
# gradient_checkpointing_kwargs:
#   use_reentrant: false
# group_by_length: false
# learning_rate: 0.0001
# liger_fused_linear_cross_entropy: false
# liger_glu_activation: false
# liger_layer_norm: false
# liger_rms_norm: false
# liger_rope: false
# 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: 4
# model_type: AutoModelForCausalLM
# num_epochs: 1
# optimizer: adamw_torch
# output_dir: ./lora-out-qwen38b-sft
# 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: qwe-3-8b-sft-run
# wandb_watch: null
# warmup_ratio: 0.05
# weight_decay: 0.0
# xformers_attention: null

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

datasets:
- chat_template: tokenizer_default
  field_messages: messages
  message_field_content: content
  message_field_role: role
  path: new_data_clean.jsonl
  roles:
    assistant:
    - assistant
    system:
    - system
    user:
    - user
  type: chat_template

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: ./nov262025-sc-LoRA-Run
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: nov-26-sc-lor-run-1
wandb_watch: null
warmup_ratio: 0.05
weight_decay: 0.0
xformers_attention: null

nov262025-sc-LoRA-Run

This model is a fine-tuned version of ByteDance-Seed/Seed-Coder-8B-Instruct on the new_data_clean.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: 8
total_train_batch_size: 128
total_eval_batch_size: 128
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: 11
training_steps: 221

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

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Model tree for pandyamarut/nov26-sc-lora

Base model

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

Finetuned

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

Adapter

(7)

this model