gemma-gguf

Model overview

Gemma 12B FABLE CODER Addition gemma-4-12B-coder-fable5-composer2.5-v1.gguf - Python coder Agent text gemma4.block_count = 48 text Input text ↓ Layer 1 ↓ Layer 2 ↓ ... ↓ Layer 48 ↓ Output probabilities text Token Score ---------------- "cat" 0.60 "dog" 0.25 "bird" 0.10 "banana" 0.05 text cat 95% dog 4% bird 1% text cat 60% dog 25% bird 10% banana 5% text cat 35% dog 28% bird 22% banana 15% text general.sampling.top_k = 64 general.sampling.top_p = 0.95 general.sampling.temp = 1.0 text Q6_K text Q2_K = very small, lower quality Q3_K Q4_K Q5_K Q6_K ← very high quality Q8_0 F16 = full precision text F16 ≈ 24 GB Q8 ≈ 13 GB Q6_K ≈ 10 GB Q5_K ≈ 8 GB Q4_K ≈ 7 GB text context_length = 262144 text attention.head_count = 16 key_length = 512 value_length = 512 text 48 Layers ┌──────────────┐ │ Layer 1 │ → 16 attention heads │ Layer 2 │ → 16 attention heads │ Layer 3 │ → 16 attention heads │ ... │ │ Layer 48 │ → 16 attention heads └──────────────┘

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：来源未披露，须进一步核验。模型介绍与第三方评测均以原来源的具体版本和方法为准。