Merge Experiments
Collection
Sorted from oldest (top) to newest (bottom) • 164 items • Updated • 4
How to use Naphula/Quantum-Precog-24B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Naphula/Quantum-Precog-24B")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Naphula/Quantum-Precog-24B")
model = AutoModelForCausalLM.from_pretrained("Naphula/Quantum-Precog-24B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))How to use Naphula/Quantum-Precog-24B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Naphula/Quantum-Precog-24B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Naphula/Quantum-Precog-24B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/Naphula/Quantum-Precog-24B
How to use Naphula/Quantum-Precog-24B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Naphula/Quantum-Precog-24B" \
--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": "Naphula/Quantum-Precog-24B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "Naphula/Quantum-Precog-24B" \
--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": "Naphula/Quantum-Precog-24B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use Naphula/Quantum-Precog-24B with Docker Model Runner:
docker model run hf.co/Naphula/Quantum-Precog-24B
⚠️ Warning: This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly, and use Mistral Tekken chat template.
This is a fully uncensored merge of pre-trained language models created using mergekit-exp.
This model was merged using the DELLA merge method using TheDrummer/Precog-24B-v1 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
architecture: MistralForCausalLM
base_model: B:\24B\!models--TheDrummer--Precog-24B-v1
models:
- model: B:\24B\!BeaverAI_Fallen-Mistral-Small-3.1-24B-v1e_textonly
parameters:
weight:
- filter: "lm_head"
value: 0.31
- filter: "o_proj"
value: 0.5
- filter: "mlp"
value: 0.31
- filter: "embed_tokens"
value: 0.31
- value: 0.31
density: 1.0
epsilon: 0.09
- model: B:\24B\!models--darkc0de--BlackXorDolphTronGOAT
parameters:
weight:
- filter: "lm_head"
value: 0.07
- filter: "o_proj"
value: 0.1
- filter: "mlp"
value: 0.07
- filter: "embed_tokens"
value: 0.07
- value: 0.07
density: 1.0
epsilon: 0.09
- model: B:\24B\!models--TheDrummer--Cydonia-24B-v4.3
parameters:
weight:
- filter: "lm_head"
value: 0.31
- filter: "o_proj"
value: 0.5
- filter: "mlp"
value: 0.31
- filter: "embed_tokens"
value: 0.31
- value: 0.31
density: 1.0
epsilon: 0.09
- model: B:\24B\Cydonia-24B-v4x-Q8_0
parameters:
weight:
- filter: "lm_head"
value: 0.31
- filter: "o_proj"
value: 0.5
- filter: "mlp"
value: 0.31
- filter: "embed_tokens"
value: 0.31
- value: 0.31
density: 1.0
epsilon: 0.09
merge_method: della
parameters:
lambda: 1.0
normalize: false
int8_mask: false
rescale: true
dtype: float32
out_dtype: bfloat16
tokenizer:
source: union
chat_template: auto
[DELLA Audit] Layer: lm_head.weight | Lambda=1.00
[BASE] !models--TheDrummer--Precog-24B-v1
!BeaverAI_Fallen-Mistral-Small-3.1-24B-v1e_textonl: █████████████████ 34.2% (W:0.31 D:1.00 N:4.43 E:0.09)
!models--TheDrummer--Cydonia-24B-v4.3 : ███████████ 24.0% (W:0.31 D:1.00 N:3.10 E:0.09)
!models--darkc0de--BlackXorDolphTronGOAT : ████████ 17.1% (W:0.07 D:1.00 N:9.78 E:0.09)
Cydonia-24B-v4x-Q8_0 : ████████████ 24.8% (W:0.31 D:1.00 N:3.21 E:0.09)
[DELLA Audit] Layer: model.layers.17.mlp.up_proj.weight | Lambda=1.00
[BASE] !models--TheDrummer--Precog-24B-v1
!BeaverAI_Fallen-Mistral-Small-3.1-24B-v1e_textonl: █████████████ 27.0% (W:0.31 D:1.00 N:1.87 E:0.09)
!models--TheDrummer--Cydonia-24B-v4.3 : ████████████ 25.5% (W:0.31 D:1.00 N:1.76 E:0.09)
!models--darkc0de--BlackXorDolphTronGOAT : ███████████ 23.0% (W:0.07 D:1.00 N:7.05 E:0.09)
Cydonia-24B-v4x-Q8_0 : ████████████ 24.5% (W:0.31 D:1.00 N:1.70 E:0.09)
[DELLA Audit] Layer: model.layers.17.self_attn.k_proj.weight | Lambda=1.00
[BASE] !models--TheDrummer--Precog-24B-v1
!BeaverAI_Fallen-Mistral-Small-3.1-24B-v1e_textonl: ██████████████ 29.1% (W:0.31 D:1.00 N:0.71 E:0.09)
!models--TheDrummer--Cydonia-24B-v4.3 : █████████████ 27.6% (W:0.31 D:1.00 N:0.67 E:0.09)
!models--darkc0de--BlackXorDolphTronGOAT : ████████ 16.6% (W:0.07 D:1.00 N:1.79 E:0.09)
Cydonia-24B-v4x-Q8_0 : █████████████ 26.8% (W:0.31 D:1.00 N:0.65 E:0.09)
[DELLA Audit] Layer: model.layers.17.self_attn.o_proj.weight | Lambda=1.00
[BASE] !models--TheDrummer--Precog-24B-v1
!BeaverAI_Fallen-Mistral-Small-3.1-24B-v1e_textonl: █████████████ 27.0% (W:0.50 D:1.00 N:0.93 E:0.09)
!models--TheDrummer--Cydonia-24B-v4.3 : ████████████ 25.4% (W:0.50 D:1.00 N:0.87 E:0.09)
!models--darkc0de--BlackXorDolphTronGOAT : ███████████ 23.2% (W:0.10 D:1.00 N:3.99 E:0.09)
Cydonia-24B-v4x-Q8_0 : ████████████ 24.4% (W:0.50 D:1.00 N:0.84 E:0.09)
[DELLA Audit] Layer: model.layers.17.self_attn.q_proj.weight | Lambda=1.00
[BASE] !models--TheDrummer--Precog-24B-v1
!BeaverAI_Fallen-Mistral-Small-3.1-24B-v1e_textonl: ██████████████ 28.1% (W:0.31 D:1.00 N:1.43 E:0.09)
!models--TheDrummer--Cydonia-24B-v4.3 : █████████████ 26.8% (W:0.31 D:1.00 N:1.36 E:0.09)
!models--darkc0de--BlackXorDolphTronGOAT : █████████ 19.2% (W:0.07 D:1.00 N:4.32 E:0.09)
Cydonia-24B-v4x-Q8_0 : ████████████ 25.9% (W:0.31 D:1.00 N:1.32 E:0.09)
[DELLA Audit] Layer: model.layers.17.self_attn.v_proj.weight | Lambda=1.00
[BASE] !models--TheDrummer--Precog-24B-v1
!BeaverAI_Fallen-Mistral-Small-3.1-24B-v1e_textonl: █████████████ 26.3% (W:0.31 D:1.00 N:0.49 E:0.09)
!models--TheDrummer--Cydonia-24B-v4.3 : ████████████ 24.7% (W:0.31 D:1.00 N:0.46 E:0.09)
!models--darkc0de--BlackXorDolphTronGOAT : ████████████ 25.4% (W:0.07 D:1.00 N:2.09 E:0.09)
Cydonia-24B-v4x-Q8_0 : ███████████ 23.6% (W:0.31 D:1.00 N:0.44 E:0.09)