Create Your ModelCYM

Build the dataset. The model IS the data — curation decides the ceiling.

1. Gather raw domain docs + any seed instruction pairs into JSONL ({"messages":[...]} chat format).
2. Near-dedup with MinHash/LSH so you do not over-train on copies.
3. Synthesize & evolve instructions from a teacher model to expand coverage; filter for diversity and quality.
4. Set a domain mixture (Strataweave) and an easy→hard ordering (Curriculord). Reuse the base tokenizer — do not retrain one unless you change languages.

from datasets import load_dataset
# 1) load your raw + seed instruction data
ds = load_dataset("json", data_files="data/kynetra_raw.jsonl")["train"]

# 2) near-dedup (MinHash) — text-dedup CLI
#    python -m text_dedup.minhash --path data/kynetra_raw.jsonl \
#       --column text --threshold 0.7 --output data/kynetra_dedup.jsonl

# 3) synthesize instructions with distilabel (Self-Instruct / Evol-Instruct)
#    then save the final SFT set as chat-formatted JSONL:
ds.to_json("data/kynetra_sft.jsonl")   # {"messages":[{"role":"user",...},{"role":"assistant",...}]}

Choose the foundation. For 99% of teams this means PICKING an open base, not training one.

1. Pick a permissively-licensed base sized to your budget — 7–9B is the sweet spot for single-GPU adaptation.
2. Prefer Apache-2.0/MIT (Qwen2.5, Mistral) for commercial/sovereign freedom.
3. Only pretrain from scratch if you have cluster budget and a genuine architecture/data reason — otherwise adaptation wins on cost and time.

from huggingface_hub import snapshot_download
# Worked example base (Apache-2.0):
snapshot_download("Qwen/Qwen2.5-7B-Instruct", local_dir="bases/qwen2.5-7b")
# Alternatives: mistralai/Mistral-7B-Instruct-v0.3, meta-llama/Llama-3.1-8B-Instruct

Specialize the frozen base cheaply — train ~0.1–1% of params with QLoRA.

1. Load the base in 4-bit (QLoRA) to fit a single GPU.
2. Attach LoRA adapters to attention + MLP projections (r=16, alpha=32 is a solid default).
3. SFT on your curated chat dataset; keep adapters small and swappable.
4. Merge adapters into the base when you are happy (GraftFold) for a single deployable checkpoint.

from unsloth import FastLanguageModel
from trl import SFTTrainer, SFTConfig

model, tok = FastLanguageModel.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct", max_seq_length=4096, load_in_4bit=True)   # NibbleGraft (QLoRA)
model = FastLanguageModel.get_peft_model(                                # RankWeave (LoRA)
    model, r=16, lora_alpha=32,
    target_modules=["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"])

SFTTrainer(model=model, tokenizer=tok,
    args=SFTConfig(output_dir="cym-sft", per_device_train_batch_size=2,
                   gradient_accumulation_steps=8, learning_rate=2e-4, num_train_epochs=2),
    train_dataset=load_dataset("json", data_files="data/kynetra_sft.jsonl")["train"]).train()

Teach taste, not just tokens — make the model prefer good answers.

1. Build preference pairs: {prompt, chosen, rejected} — from human ranks, an LLM judge, or best-of-N rejection sampling.
2. Run DPO from your SFT checkpoint (DPO needs no separate reward model — simpler and stable).
3. Use ORPO if you want to fold alignment into one SFT-style pass; KTO if your data is unpaired.
4. Watch the preference margin AND KL from the reference — high margin + high KL means reward hacking.

from trl import DPOTrainer, DPOConfig
from datasets import load_dataset

pref = load_dataset("json", data_files="data/kynetra_pref.jsonl")["train"]  # prompt/chosen/rejected
DPOTrainer(model="cym-sft",
    args=DPOConfig(beta=0.1, output_dir="cym-dpo", learning_rate=5e-6, num_train_epochs=1),
    train_dataset=pref).train()

Optional — fuse skills or pour a big model into a small one.

1. Merge multiple domain adapters/checkpoints into one with TIES (sign-aware) when you need several skills in one model.
2. Average several runs of the SAME base (soup) for a flatter, more general checkpoint.
3. Distill a larger teacher into your 7B when you want its behavior at lower serving cost.
4. Skip this stage entirely if a single adapter already meets quality.

# mergekit TIES config (merge two domain checkpoints onto the base)
models:
  - model: cym-dpo
    parameters: { weight: 0.6, density: 0.7 }
  - model: cym-ops-adapter
    parameters: { weight: 0.4, density: 0.7 }
merge_method: ties
base_model: Qwen/Qwen2.5-7B-Instruct
dtype: bfloat16
# run:  mergekit-yaml merge.yml cym-merged

Shrink for serving — 4-bit weights, a fraction of the memory, near-equal quality.

1. For GPU serving: produce an AWQ build — vLLM runs it on the fast Marlin kernel.
2. For laptops/edge: convert to GGUF and quantize to Q4_K_M for llama.cpp / Ollama.
3. Calibrate on a small in-domain sample so quantization error lands where it matters least.

# A) AWQ for vLLM (llm-compressor)
python -c "from llmcompressor import oneshot; \
  oneshot(model='cym-merged', recipe='awq', output_dir='cym-awq')"

# B) GGUF Q4_K_M for local (llama.cpp)
python llama.cpp/convert_hf_to_gguf.py cym-merged --outfile cym.gguf
./llama.cpp/llama-quantize cym.gguf cym-Q4_K_M.gguf Q4_K_M

Give the model the knowledge it was never trained on — ground answers in your data.

1. Chunk and embed your knowledge base; store vectors in a DB.
2. At query time: retrieve top-k by vector similarity, rerank with a cross-encoder, then prepend the survivors to the prompt.
3. Retrieval recall is the ceiling — invest in chunking and reranking before bigger models.
4. Extend context (YaRN/RoPE) only with a short long-context fine-tune; raw interpolation degrades quality.

from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient

emb = SentenceTransformer("BAAI/bge-base-en-v1.5")          # Echograph
qc = QdrantClient(":memory:")                               # Memvault store
# index chunks -> qc.upsert(...); at query time:
hits = qc.search("kb", emb.encode("how do I reset billing?").tolist(), limit=20)
# rerank top-20 with a cross-encoder (Graftsieve), keep top-4, prepend to prompt

Tokens per dollar — expose the model behind a fast, OpenAI-compatible API.

1. Serve the AWQ build on vLLM — you get PagedAttention + continuous batching for free.
2. Add a small draft model for speculative decoding if latency matters and outputs are low-temperature.
3. For local/offline, run the GGUF build via Ollama or llama.cpp server.

# GPU production (OpenAI-compatible at :8000/v1)
vllm serve cym-awq --quantization awq_marlin --max-model-len 8192 --port 8000

# Local / edge
ollama create cym -f Modelfile   # FROM ./cym-Q4_K_M.gguf
ollama run cym

If you can't measure it, you can't ship it. Score before and after every change.

1. Run standardized benchmarks against the served endpoint for a comparable baseline.
2. Use an LLM judge with rubric + position-swap for task-specific quality; RAGAS for retrieval faithfulness.
3. Wire DeepEval into CI so regressions block releases; monitor production drift continuously.

# Benchmark the live model via vLLM backend
lm_eval --model local-completions \
  --model_args base_url=http://localhost:8000/v1,model=cym-awq \
  --tasks mmlu,gsm8k,ifeval --batch_size auto

# RAG faithfulness / answer-relevancy with RAGAS, judge quality with DeepEval in CI.

Train on your terms, behind your walls — guardrails, sharding, sovereign deploy.

1. Wrap the endpoint with input/output guardrails and jailbreak/prompt-injection defenses.
2. For big training, shard with FSDP/ZeRO-3 across GPUs; checkpoint with RNG + data-position state so runs resume exactly.
3. For regulated/sovereign work, run the whole pipeline air-gapped against a mirrored artifact registry.

# Guardrail in front of the endpoint (Llama Guard pattern): classify -> allow/deny -> model
# Sharded training when you outgrow one GPU (TorchTune FSDP2):
tune run --nproc_per_node 8 full_finetune_distributed --config qwen2_5_7B_full
# Anchorpoint: enable resumable checkpoints (weights + optimizer + RNG + dataloader position).