Instructions to use Jab1718/qwen3.8-flash-coder-selective-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jab1718/qwen3.8-flash-coder-selective-int8 with Transformers:

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

pipe = pipeline("text-generation", model="Jab1718/qwen3.8-flash-coder-selective-int8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jab1718/qwen3.8-flash-coder-selective-int8")
model = AutoModelForCausalLM.from_pretrained("Jab1718/qwen3.8-flash-coder-selective-int8", 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 Jab1718/qwen3.8-flash-coder-selective-int8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jab1718/qwen3.8-flash-coder-selective-int8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jab1718/qwen3.8-flash-coder-selective-int8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jab1718/qwen3.8-flash-coder-selective-int8

SGLang

How to use Jab1718/qwen3.8-flash-coder-selective-int8 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 "Jab1718/qwen3.8-flash-coder-selective-int8" \
    --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": "Jab1718/qwen3.8-flash-coder-selective-int8",
		"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 "Jab1718/qwen3.8-flash-coder-selective-int8" \
        --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": "Jab1718/qwen3.8-flash-coder-selective-int8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jab1718/qwen3.8-flash-coder-selective-int8 with Docker Model Runner:
```
docker model run hf.co/Jab1718/qwen3.8-flash-coder-selective-int8
```

qwen3.8-flash-coder-selective-int8 / config.json

Jab1718

Upload Qwen3.8-Flash-Coder (Selective INT8 Quantized ~19.7GB)

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	{
	"architectures": [
	"Qwen4ExpForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 248044,
	"dtype": "bfloat16",
	"eos_token_id": 248044,
	"full_attention_interval": 4,
	"hc_count": 4,
	"hc_lowrank": 320,
	"head_dim": 256,
	"heads_per_ngram": 8,
	"hidden_act": "silu",
	"hidden_size": 2560,
	"indexer_budget": 2048,
	"indexer_compress_ratio": 4,
	"indexer_head_dim": 128,
	"indexer_kv_heads": 1,
	"indexer_n_heads": 4,
	"initializer_range": 0.02,
	"language_model_only": true,
	"layer_types": [
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"qwen_sparse_attention"
	],
	"linear_conv_kernel_dim": 4,
	"linear_key_head_dim": 128,
	"linear_num_key_heads": 16,
	"linear_num_value_heads": 48,
	"linear_value_head_dim": 128,
	"make_ngram_vocab_size_divisible_by": 128,
	"mamba_ssm_dtype": "float32",
	"max_position_embeddings": 262144,
	"model_type": "qwen4_exp_text",
	"moe_intermediate_size": 640,
	"mtp": {
	"hybrid": true,
	"layer_types": [
	"full_attention"
	],
	"mtp_use_hidden_state_from_layer": null,
	"num_hidden_layers": 1,
	"rope_theta": 10000000
	},
	"mtp_num_hidden_layers": 1,
	"mtp_use_dedicated_embeddings": false,
	"ngram_size": 3,
	"ngram_vocab_size_base": 20000000,
	"norm_topk_prob": true,
	"num_attention_heads": 24,
	"num_experts": 128,
	"num_experts_per_tok": 10,
	"num_hidden_layers": 48,
	"num_key_value_heads": 2,
	"number_of_conv_states": 1,
	"output_gate_type": "sigmoid",
	"output_router_logits": false,
	"pad_token_id": null,
	"partial_rotary_factor": 0.25,
	"ple_conv_kernel_size": 4,
	"ple_embed_dim": 2560,
	"ple_layer_ids": [],
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"mrope_interleaved": true,
	"mrope_section": [
	11,
	11,
	10
	],
	"partial_rotary_factor": 0.25,
	"rope_theta": 10000000,
	"rope_type": "default"
	},
	"router_aux_loss_coef": 0.001,
	"seed": 1234,
	"shared_expert_intermediate_size": 640,
	"split_ngram_parts": 128,
	"tie_word_embeddings": false,
	"transformers_version": "5.16.1",
	"use_cache": true,
	"vocab_size": 248320
	}