jdfelo

gemma-4-31B-v2-NVFP4

What this is

Weights quantized to NVFP4 (FP4, group size 16) with LLM Compressor.
~62.5 GB (bf16) → ~23 GB. Fits a single Blackwell GPU with plenty of headroom for a long-context KV cache. (The NVFP4 language model is ~16 GB; the vision tower + tied embeddings stay bf16, adding ~7 GB.)
Vision tower, embeddings, and lm_head kept at bf16 — only the language-model Linear layers are quantized, so multimodal understanding is untouched.
Abliterated: the base finetune has standard safety refusals removed — see Responsible use.
Parameter count: 32.7 B (same as the base model). Hugging Face's widget shows ~20B because it counts stored tensor elements and cannot see that NVFP4 packs two 4-bit weights per byte — the 14.6 B packed bytes are really 29.3 B params, plus 3.4 B bf16 params. This undercount is normal for every NVFP4/packed-4-bit checkpoint; no weights are missing (all 60 layers + vision tower + embeddings are present).

Quantization details

Table

Scheme	NVFP4, `targets="Linear"`, group size 16
Ignored (kept bf16)	`re:.vision.`, `re:.audio.`, `lm_head`, `re:.embed.`
Tooling	llm-compressor 0.12.0 · compressed-tensors 0.17.1 · transformers 5.10.1 · torch 2.11.0+cu128
Calibration	512 samples @ 2048 tokens, `HuggingFaceH4/ultrachat_200k` (text-only)

The ignore set and scheme mirror the reference RedHatAI/gemma-4-31B-it-NVFP4 recipe for the base architecture.

Serving with vLLM (Blackwell)

bash

vllm serve jdfelo/gemma-4-31B-v2-NVFP4 \  --max-model-len 32768 \  --gpu-memory-utilization 0.90 \  --kv-cache-dtype fp8 \  --limit-mm-per-prompt '{"image":0,"audio":0}' \  --async-scheduling

For maximum single-stream tok/s, add EAGLE3 speculative decoding:

bash

  --speculative-config '{"model":"RedHatAI/gemma-4-31B-it-speculator.eagle3","num_speculative_tokens":3,"method":"eagle3"}'

The EAGLE3 draft is trained on the base google/gemma-4-31B-it; acceptance rate on this abliterated finetune may differ — smoke-test and drop to num_speculative_tokens: 2 if it's low.

Requirements

A Blackwell GPU with FP4 tensor cores: 1× B200 (180 GB) or 1× RTX PRO 6000 (96 GB). Weights are ~23 GB; the rest is KV cache.
On pre-Blackwell GPUs, NVFP4 loads via a Marlin fallback (smaller footprint, not faster than FP8).

Client (OpenAI-compatible)

python

from openai import OpenAIclient = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")r = client.chat.completions.create(    model="jdfelo/gemma-4-31B-v2-NVFP4",    messages=[{"role": "user", "content": "Explain speculative decoding in one paragraph."}],    max_tokens=512, temperature=0.7,)print(r.choices[0].message.content)

Responsible use

This is an abliterated / uncensored model: standard safety refusals have been removed from the base finetune. You are solely responsible for how you use it and for compliance with applicable law and the terms of the upstream models.

Lineage & license

Quantized from jdfelo/gemma-4-31B-v2, a finetune/abliteration of google/gemma-4-31B-it. Released under the MIT license by the repository owner. Note that upstream Gemma components remain subject to Google's Gemma Terms of Use; downstream users should review that lineage.

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Model Details

Model Provider

jdfelo

Model Tree

Base

jdfelo/gemma-4-31B-v2

Quantized

this model

Input Modalities

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Output Modalities

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Supported Functionality

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What this is

Weights quantized to NVFP4 (FP4, group size 16) with LLM Compressor.
~62.5 GB (bf16) → ~23 GB. Fits a single Blackwell GPU with plenty of headroom for a long-context KV cache. (The NVFP4 language model is ~16 GB; the vision tower + tied embeddings stay bf16, adding ~7 GB.)
Vision tower, embeddings, and lm_head kept at bf16 — only the language-model Linear layers are quantized, so multimodal understanding is untouched.
Abliterated: the base finetune has standard safety refusals removed — see Responsible use.
Parameter count: 32.7 B (same as the base model). Hugging Face's widget shows ~20B because it counts stored tensor elements and cannot see that NVFP4 packs two 4-bit weights per byte — the 14.6 B packed bytes are really 29.3 B params, plus 3.4 B bf16 params. This undercount is normal for every NVFP4/packed-4-bit checkpoint; no weights are missing (all 60 layers + vision tower + embeddings are present).

Quantization details

Table

Scheme	NVFP4, `targets="Linear"`, group size 16
Ignored (kept bf16)	`re:.vision.`, `re:.audio.`, `lm_head`, `re:.embed.`
Tooling	llm-compressor 0.12.0 · compressed-tensors 0.17.1 · transformers 5.10.1 · torch 2.11.0+cu128
Calibration	512 samples @ 2048 tokens, `HuggingFaceH4/ultrachat_200k` (text-only)

The ignore set and scheme mirror the reference RedHatAI/gemma-4-31B-it-NVFP4 recipe for the base architecture.

Serving with vLLM (Blackwell)

bash

vllm serve jdfelo/gemma-4-31B-v2-NVFP4 \  --max-model-len 32768 \  --gpu-memory-utilization 0.90 \  --kv-cache-dtype fp8 \  --limit-mm-per-prompt '{"image":0,"audio":0}' \  --async-scheduling

vllm serve jdfelo/gemma-4-31B-v2-NVFP4 \  --max-model-len 32768 \  --gpu-memory-utilization 0.90 \  --kv-cache-dtype fp8 \  --limit-mm-per-prompt '{"image":0,"audio":0}' \  --async-scheduling

For maximum single-stream tok/s, add EAGLE3 speculative decoding:

bash

  --speculative-config '{"model":"RedHatAI/gemma-4-31B-it-speculator.eagle3","num_speculative_tokens":3,"method":"eagle3"}'

  --speculative-config '{"model":"RedHatAI/gemma-4-31B-it-speculator.eagle3","num_speculative_tokens":3,"method":"eagle3"}'

The EAGLE3 draft is trained on the base google/gemma-4-31B-it; acceptance rate on this abliterated finetune may differ — smoke-test and drop to num_speculative_tokens: 2 if it's low.

Requirements

A Blackwell GPU with FP4 tensor cores: 1× B200 (180 GB) or 1× RTX PRO 6000 (96 GB). Weights are ~23 GB; the rest is KV cache.
On pre-Blackwell GPUs, NVFP4 loads via a Marlin fallback (smaller footprint, not faster than FP8).

Client (OpenAI-compatible)

python

from openai import OpenAIclient = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")r = client.chat.completions.create(    model="jdfelo/gemma-4-31B-v2-NVFP4",    messages=[{"role": "user", "content": "Explain speculative decoding in one paragraph."}],    max_tokens=512, temperature=0.7,)print(r.choices[0].message.content)

from openai import OpenAIclient = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")r = client.chat.completions.create(    model="jdfelo/gemma-4-31B-v2-NVFP4",    messages=[{"role": "user", "content": "Explain speculative decoding in one paragraph."}],    max_tokens=512, temperature=0.7,)print(r.choices[0].message.content)

gemma-4-31B-v2-NVFP4

README

What this is

Quantization details

Serving with vLLM (Blackwell)

Requirements

Client (OpenAI-compatible)

Responsible use

Lineage & license

Explore FriendliAI today

README

What this is

Quantization details

Serving with vLLM (Blackwell)

Requirements

Client (OpenAI-compatible)

Responsible use

Lineage & license