GGUF quantizations of
artindnr/chatberry
, the flagship release of the ChatBerry family — a direct-answer chat fine-tune of
artindnr/strawberry-1
(itself built on
openai/gpt-oss-20b
). ChatBerry's reasoning ("thinking") channel is disabled, so it responds directly instead of emitting a separate chain-of-thought trace.
These quants let you run ChatBerry locally with
llama.cpp
, Ollama, LM Studio, or any other GGUF-compatible runtime.
Files
Bits
Quant
Size
Notes
2-bit
chatberry.Q2_K.gguf
12.1 GB
Smallest, most quality loss — only for tight memory budgets
3-bit
chatberry.Q3_K_S.gguf
12.1 GB
Small, low quality tier
3-bit
chatberry.Q3_K_M.gguf
12.9 GB
Balanced within the 3-bit tier
3-bit
chatberry.Q3_K_L.gguf
13.3 GB
Largest/highest quality of the 3-bit quants
4-bit
chatberry.IQ4_XS.gguf
12.2 GB
Compact 4-bit variant, good quality-per-GB
4-bit
chatberry.Q4_K_S.gguf
14.7 GB
Solid general-purpose 4-bit quant
4-bit
chatberry.Q4_K_M.gguf
15.8 GB
Recommended default — good balance of quality and size
5-bit
chatberry.Q5_K_S.gguf
15.9 GB
Higher fidelity, moderate size increase
5-bit
chatberry.Q5_K_M.gguf
16.9 GB
Best 5-bit option if you have the headroom
6-bit
chatberry.Q6_K.gguf
22.2 GB
Near-lossless, larger file
8-bit
chatberry.Q8_0.gguf
22.3 GB
Highest quality of the set, closest to full precision
If you're unsure which to pick:
Q4_K_M
is a solid default for most setups. Go up to
Q5_K_M
,
Q6_K
, or
Q8_0
if you have the VRAM/RAM to spare and want maximum fidelity, and drop to
Q3_K_M
/
Q2_K
if you're tightly memory-constrained.
Usage
llama.cpp
./llama-cli -m chatberry.Q4_K_M.gguf -p "تو کی هستی و اسمت چیه؟" -n 512
Or serve it as an OpenAI-compatible endpoint:
./llama-server -m chatberry.Q4_K_M.gguf -c 4096
Ollama
Create a
Modelfile
:
FROM ./chatberry.Q4_K_M.gguf
Then:
ollama create chatberry -f Modelfile
ollama run chatberry
LM Studio
Download the
.gguf
file of your choice directly in LM Studio's model browser (search
artindnr/chatberry-gguf
), or drop the file into your local models folder.
About ChatBerry
ChatBerry is the culmination of the iterative ChatBerry line (1.0 → 1.1 → 1.2 → ChatBerry), each release trained on more data than the last. It converts the reasoning behavior of
strawberry-1
into a direct-answer chat model — no visible analysis/chain-of-thought channel, just a final response.
ChatBerry uses the
gpt-oss
chat template (Harmony format) inherited from its base model. Most GGUF runtimes (
llama.cpp
, Ollama, LM Studio) apply this automatically when loading the model — no need to set a reasoning-language system message or parse separate channels; ChatBerry goes straight to its final answer.
Limitations
Quantization introduces some precision loss versus the original bf16 weights — expect small quality differences across the tiers, most noticeable at Q2_K/Q3_K.
ChatBerry trades away Strawberry-1's explicit chain-of-thought reasoning; for tasks that benefit from visible step-by-step reasoning,
artindnr/strawberry-1
may be a better fit.
Inherits the general capabilities and limitations of the
gpt-oss-20b
base model and
strawberry-1
, including the possibility of hallucinated facts.
No formal safety fine-tuning beyond what is inherited from the base model and Strawberry-1 has been applied; use appropriate safeguards in production settings.
License
Released under the
Apache 2.0
license, consistent with the base
gpt-oss-20b
model,
strawberry-1
, and
chatberry
.
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