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cass-vibe-8k

README

License: apache-2.0

Quantization Details

Quantization was performed using llmcompressor with the following recipe:

yaml

default_stage:  default_modifiers:    QuantizationModifier:      targets: [Linear]      ignore: ['re:.*lm_head', 're:.*embed_tokens$', 're:.*visual.*', 're:.*model.visual.*',        're:.*linear_attn.*']      scheme: FP8_DYNAMIC      bypass_divisibility_checks: false      requires_calibration_data: false

Linear layers are quantized to FP8 with dynamic per-tensor activation scaling, so no calibration dataset is required (requires_calibration_data: false). The lm_head, embedding table, any vision-tower (visual) components, and linear_attn layers are excluded from quantization and remain at full precision to preserve output-head fidelity and numerical stability.

Table

Base model	prithivMLmods/Qwen3.5-9B-DS-v4-Flash-v3.0
Quantization scheme	FP8_DYNAMIC (Linear layers only)
Format	compressed-tensors
Calibration data required	No (dynamic activation scaling)
Excluded from quantization	lm_head, embed_tokens, visual (if present), linear_attn

Use with vLLM

Qwen3.5-9B-DS-v4-Flash-v3.0-FP8 is served through vLLM with native support for compressed-tensors FP8 checkpoints.

Requirements

torch >= 2.11.0
vllm >= 0.19.1
A GPU with FP8 support recommended (Hopper or Blackwell class) for best throughput; also runs on Ampere with FP8 dequantized on the fly.

Serve

bash

vllm serve prithivMLmods/Qwen3.5-9B-DS-v4-Flash-v3.0-FP8 \  --max-model-len 32768

Client request

python

from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY") messages = [    {        "role": "user",        "content": "Explain how a transformer model processes text."    }] response = client.chat.completions.create(    model="prithivMLmods/Qwen3.5-9B-DS-v4-Flash-v3.0-FP8",    messages=messages,    temperature=0.0,    max_tokens=512,) print(response.choices[0].message.content)

Quick Start with Transformers

bash

pip install transformers accelerate

python

from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model = AutoModelForCausalLM.from_pretrained(    "prithivMLmods/Qwen3.5-9B-DS-v4-Flash-v3.0-FP8",    torch_dtype="auto",    device_map="auto") tokenizer = AutoTokenizer.from_pretrained(    "prithivMLmods/Qwen3.5-9B-DS-v4-Flash-v3.0-FP8") messages = [    {        "role": "user",        "content": "Explain how a transformer model processes text."    }] inputs = tokenizer.apply_chat_template(    messages,    tokenize=True,    add_generation_prompt=True,    return_tensors="pt").to(model.device) outputs = model.generate(    inputs,    max_new_tokens=512) print(    tokenizer.decode(        outputs[0][inputs.shape[-1]:],        skip_special_tokens=True    ))

Training Details (Base Model)

Table with columns: Setting, Value
Setting	Value
Base Model	prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0
Original Backbone	Qwen/Qwen3.5-9B
Training Method	Multi-stage Supervised Fine-Tuning (SFT)
Maximum Sequence Length	32,768 tokens (Long Context)
Training Precision	BF16 (Full Precision)
Training & Alignment Framework	TRL
Training Datasets	, , and additional high-quality reasoning datasets

Intended Use and Limitations

Intended use, known limitations, training data composition, and responsible use guidance are unchanged from the base model. See the Qwen3.5-9B-DS-v4-Flash-v3.0 model card for full details.

Reasoning Research: Studying long-context reasoning and multi-stage training techniques.
Mathematical Reasoning: Solving complex mathematical problems with multi-step reasoning.
Scientific Reasoning: Performing structured scientific analysis and problem solving.
Coding Assistance: Improving code understanding and generation through long-context reasoning.
Instruction Following: Evaluating and improving instruction-following capabilities.
Efficient Local Deployment: Reduced memory footprint enables 9B reasoning inference on smaller GPUs.

Limitations

Experimental Model: Behavior may differ from the base model in certain scenarios.
Reasoning Artifacts: Complex reasoning chains may occasionally produce incorrect intermediate steps or conclusions.
Training Biases: Performance reflects the characteristics and coverage of the reasoning datasets used during training.
Quantization Drift: FP8 dynamic quantization may introduce minor numerical differences relative to the bf16 base model; downstream accuracy should be validated for production use cases.

License

Released under the Apache 2.0 License, consistent with the base model.

Acknowledgements

Qwen/Qwen3.5-9B: Base model used for this project.
TRL - Transformers Reinforcement Learning: TRL is a full stack library providing tools to train transformer language models with methods including Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more.
Transformers: Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.
llmcompressor: Used to produce the FP8 dynamic quantization for this release.

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Model Details

Model Provider

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Model Tree

Base

prithivMLmods/Qwen3.5-9B-DS-v4-Flash-v3.0

Quantized

this model

Input Modalities

TextImageVideo

Output Modalities

Text

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README

License: apache-2.0

Quantization Details

Quantization was performed using llmcompressor with the following recipe:

yaml

default_stage:  default_modifiers:    QuantizationModifier:      targets: [Linear]      ignore: ['re:.*lm_head', 're:.*embed_tokens$', 're:.*visual.*', 're:.*model.visual.*',        're:.*linear_attn.*']      scheme: FP8_DYNAMIC      bypass_divisibility_checks: false      requires_calibration_data: false

default_stage:  default_modifiers:    QuantizationModifier:      targets: [Linear]      ignore: ['re:.*lm_head', 're:.*embed_tokens$', 're:.*visual.*', 're:.*model.visual.*',        're:.*linear_attn.*']      scheme: FP8_DYNAMIC      bypass_divisibility_checks: false      requires_calibration_data: false

Table

Base model	prithivMLmods/Qwen3.5-9B-DS-v4-Flash-v3.0
Quantization scheme	FP8_DYNAMIC (Linear layers only)
Format	compressed-tensors
Calibration data required	No (dynamic activation scaling)
Excluded from quantization	lm_head, embed_tokens, visual (if present), linear_attn

Use with vLLM

Qwen3.5-9B-DS-v4-Flash-v3.0-FP8 is served through vLLM with native support for compressed-tensors FP8 checkpoints.

Requirements

torch >= 2.11.0
vllm >= 0.19.1
A GPU with FP8 support recommended (Hopper or Blackwell class) for best throughput; also runs on Ampere with FP8 dequantized on the fly.

Serve

bash

vllm serve prithivMLmods/Qwen3.5-9B-DS-v4-Flash-v3.0-FP8 \  --max-model-len 32768

Client request

python

from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY") messages = [    {        "role": "user",        "content": "Explain how a transformer model processes text."    }] response = client.chat.completions.create(    model="prithivMLmods/Qwen3.5-9B-DS-v4-Flash-v3.0-FP8",    messages=messages,    temperature=0.0,    max_tokens=512,) print(response.choices[0].message.content)

Quick Start with Transformers

bash

pip install transformers accelerate

python

from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model = AutoModelForCausalLM.from_pretrained(    "prithivMLmods/Qwen3.5-9B-DS-v4-Flash-v3.0-FP8",    torch_dtype="auto",    device_map="auto") tokenizer = AutoTokenizer.from_pretrained(    "prithivMLmods/Qwen3.5-9B-DS-v4-Flash-v3.0-FP8") messages = [    {        "role": "user",        "content": "Explain how a transformer model processes text."    }] inputs = tokenizer.apply_chat_template(    messages,    tokenize=True,    add_generation_prompt=True,    return_tensors="pt").to(model.device) outputs = model.generate(    inputs,    max_new_tokens=512) print(    tokenizer.decode(        outputs[0][inputs.shape[-1]:],        skip_special_tokens=True    ))

Training Details (Base Model)

Table with columns: Setting, Value
Setting	Value
Base Model	prithivMLmods/Q3.5-9B-DS-v4-Flash-v2.0
Original Backbone	Qwen/Qwen3.5-9B
Training Method	Multi-stage Supervised Fine-Tuning (SFT)
Maximum Sequence Length	32,768 tokens (Long Context)
Training Precision	BF16 (Full Precision)
Training & Alignment Framework	TRL
Training Datasets	, , and additional high-quality reasoning datasets

Intended Use and Limitations

Intended use, known limitations, training data composition, and responsible use guidance are unchanged from the base model. See the Qwen3.5-9B-DS-v4-Flash-v3.0 model card for full details.

Reasoning Research: Studying long-context reasoning and multi-stage training techniques.
Mathematical Reasoning: Solving complex mathematical problems with multi-step reasoning.
Scientific Reasoning: Performing structured scientific analysis and problem solving.
Coding Assistance: Improving code understanding and generation through long-context reasoning.
Instruction Following: Evaluating and improving instruction-following capabilities.
Efficient Local Deployment: Reduced memory footprint enables 9B reasoning inference on smaller GPUs.

Limitations

Experimental Model: Behavior may differ from the base model in certain scenarios.
Reasoning Artifacts: Complex reasoning chains may occasionally produce incorrect intermediate steps or conclusions.
Training Biases: Performance reflects the characteristics and coverage of the reasoning datasets used during training.
Quantization Drift: FP8 dynamic quantization may introduce minor numerical differences relative to the bf16 base model; downstream accuracy should be validated for production use cases.

License

Released under the Apache 2.0 License, consistent with the base model.

Acknowledgements

Qwen/Qwen3.5-9B: Base model used for this project.
TRL - Transformers Reinforcement Learning: TRL is a full stack library providing tools to train transformer language models with methods including Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more.
Transformers: Transformers provides state-of-the-art machine learning models for text, computer vision, audio, video, and multimodal tasks, supporting both inference and training.
llmcompressor: Used to produce the FP8 dynamic quantization for this release.